-----2
1. Alajlan, N., Kamel, M., Freeman, G.: Multi-object image retrieval based on shape and topology. Signal Processing: Image Communication 21(10), 904918 (2006) 
2. Brodley, A., Lane, T.: Creating and exploiting coverage and diversity. In: Proceed- ings of AAAI 1996 Workshop on Integrating Multiple Learned Models, Portland, OR, pp. 814 (1996) 
3. Chang, C.C., Lin, C.J.: Libsvm: A library for support vector machines. ACM Trans- actions on Intelligent Systems and Technology 2(3), 127 (2011) 
4. Ciocca, G., Schettini, R.: Content-based similarity retrieval of trademarks using relevance feedback. Pattern Recognition 34(8), 16391655 (2001) 
5. Cortelazzo, G., Mian, G., Vezzi, G., Zamperoni, P.: Trademark shapes description by string-matching techniques. Pattern Recognition 27(8), 10051018 (1994) 
6. Dempster, A.: Upper and lower probabilities induced by multivalued mappings.Annals of Mathematical Statistics 38(2), 325339 (1967) 
7. Dietterich, T.G.: Machine learning research: Four current directions. Artificial In- tell. Mag. 18(4), 97136 (1997) 
8. Doermann, D., Rivlin, E., Weiss, I.: Applying algebraic and differential invariants for logo recognition. Machine Vision and Applications 9(2), 7386 (1996) 
9. Escalera, S., Fornes, A., Pujol, O., Llados, J., Radeva, P.: Circular blurred shape model for multiclass symbol recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 41(2), 497506 (2011) 
10. Freund, Y., Schapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119139 (1997) 
11. Garc?a-Pedrajas, N., Ortiz-Boyer, D.: An empirical study of binary classifier fusion methods for multiclass classification. Information Fusion 12(2), 111130 (2011) 
12. Ho, T.K.: The random subspace method for constructing decision forests. IEEE Transactions on Pattem Analysis and Machine Intelligence 20, 832844 (1998) 
13. Hwang, S., Kim, W.: A novel approach to the fast computation of zernike moments.Pattern Recognition 39(11), 20652076 (2006) 
14. Jain, A., Vailaya, A.: Shape-based retrieval: A case study with trademark image databases. Pattern Recognition 31(9), 13691390 (1998) 
15. Kim, Y., Kim, W.: Content-based trademark retrieval system using a visually salient feature. Image and Vision Computing 16(12-13), 931939 (1998) 
16. Kittler, J., Hatef, M., Duin, R., Matas, J.: On combining classifiers. IEEE Trans- actions on Pattern Analysis and Machine Inteligence 20(3), 226239 (1998) 
17. Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley, New York (2004) 
18. Li, S., Lee, M., Pun, C.: Complex zernike moments features for shape-based image retrieval. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 39(1), 227237 (2009) 
19. Mohd Anuar, F., Setchi, R., Lai, Y.K.: Trademark image retrieval using an inte- grated shape descriptor. Expert Systems with Applications (2012), http://dx.doi.org/10.1016/j.eswa.2012.07.031 
20. Neumann, J., Samet, H., Soffer, A.: Integration of local and global shape analysis for logo classification. Pattern Recognition Letters 23, 14491457 (2002) 
21. Opitz, D.W.: Feature selection for ensembles (1999) 
22. Polikar, R.: Ensemble based systems in decision making. IEEE Circuits and Sys- tems Magazine 6(3), 2145 (2006) 12 M.A. Bagheri, Q. Gao, and S. Escalera 
23. Qi, H., Li, K., Shen, Y., Qu, W.: An effective solution for trademark image retrieval by combining shape description and feature matching. Pattern Recognition 43(6), 20172027 (2010) 
24. Rogova, G.: Combining the results of several neural network classifiers. Neural Networks 7, 777781 (1994) 
25. Seiden, S., Dillencourt, M., Irani, S., Borrey, R., Murphy, T.: Logo detection in document images. In: International Conference on Imaging Science, Systems, and Technology, Las Vegas, Nevada, pp. 446449 (1997) 
26. Zhang, D., Lu, G.: Shape-based image retrieval using generic fourier descriptor.Signal Processing: Image Communication 17(10), 825848 (2002) 
27. Zhang, D., Lu, G.: Study and evaluation of different fourier methods for image retrieval. Image and Vision Computing 23(1), 3349 (2005) 
28. Zhang, D., Lu, G.: Review of shape representation and description techniques.Pattern Recognition 37(1), 119 (2004) 
-----2
1. Butz, C.J., Hua, S., Konkel, K., Yao, H.: Join Tree Propagation with Prioritized Messages. Networks 55(4), 350359 (2010) 
2. Butz, C.J., Konkel, K., Lingras, P.: Join Tree Propagation Utilizing both Arc Reversal and Variable Elimination. Int. J. Approx. Reasoning 52(7), 948959 (2011) 
3. Butz, C.J., Yan, W., Lingras, P., Yao, Y.Y.: The CPT Structure of Variable Elimi- nation in Discrete Bayesian Networks. In: Ras, Z.W., Tsay, L.S. (eds.) Advances in Intelligent Information Systems. SCI, vol. 265, pp. 245257. Springer, Heidelberg (2010) 
4. Castillo, E., Gutierrez, J., Hadi, A.: Expert Systems and Probabilistic Network Models. Springer, New York (1997) 
5. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms.MIT Press, Cambridge (2009) 
6. Darwiche, A.: Modeling and Reasoning with Bayesian Networks. Cambridge Uni- versity Press, New York (2009) 
7. Kjaerulff, U.B., Madsen, A.L.: Bayesian Networks and Influence Diagrams.Springer, New York (2008) 
8. Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Tech- niques. MIT Press, Cambridge (2009) 
9. Madsen, A.L.: A Differential Semantics of Lazy AR Propagation. In: 21st Confer- ence on Uncertainty in Artificial Intelligence, pp. 364371. Morgan Kaufmann, San Mateo (2005) 
10. Madsen, A.L.: Improvements to Message Computation in Lazy Propagation. Int.J. Approximate Reasoning 51(5), 499514 (2010) 
11. Meek, C.: Strong Completeness and Faithfulness in Bayesian Networks. In: 11th Conference on Uncertainty in Artificial Intelligence, pp. 411418. Morgan Kauf- mann, San Mateo (1995) 
12. Pearl, J.: Fusion, Propagation and Structuring in Belief Networks. Artif. Intell. 29, 241288 (1986) 
13. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1988) 
14. Pearl, J.: Belief Networks Revisited. Artif. Intell. 59, 4956 (1993) 
15. Shafer, G.: Probabilistic Expert Systems. SIAM, Philadelphia (1996) 
16. Wong, S.K.M., Butz, C.J., Wu, D.: On the Implication Problem for Probabilis- tic Conditional Independency. IEEE Trans. Syst. Man Cybern. A 30(6), 785805 (2000) 
17. Zhang, N.L., Poole, D.: A Simple Approach to Bayesian Network Computations.In: 7th Canadian Conference on Artificial Intelligence, pp. 171178. Springer, New York (1994) 
-----1
[1] Balicco, L., Paganelli, C.: Access to health information: going from professional to public  practices. In: 4th Int. Conference on Information Systems and Economic Intelligence,  SIIE 2011 (2011)  
[2] Campbell, J., Xu, J., Wah Fung, K.: Can SNOMED CT fulfill the vision of a  compositional terminology? Analyzing the use case for Problem List. In: AMIA Annual  Symposium Proc. 2011, pp. 181188 (2011)  
[3] Carroll, J., Koeling, R., Puri, S.: Lexical Acquisition for Clinical Text Mining Using  Distributional Similarity. In: Gelbukh, A. (ed.) CICLing 2012, Part II. LNCS, vol. 7182,  pp. 232246. Springer, Heidelberg (2012)  
[4] Dietterich, T.G.: Approximate Statistical Tests for Comparing Supervised Classification  Learning Algorithms. Neural Computation 10(7), 18951924 (1998)  
[5] Dwyer, C., Hiltz, S.R., Passerini, K.: Trust and privacy concern within social networking  sites: A comparison of Facebook and MySpace. In: Proceedings of the Thirteenth  Americas Conference on Information Systems, Keystone, Colorado, August 09-12 (2007)  
[6] Grace, J., Gruhl, D., Haas, K., Nagarajan, M., Robson, C., Sahoo, N.: Artist ranking  through analysis of on-line community comments (2007),   http://domino.research.ibm.com/library/cyberdig.nsf/  papers/E50790E50756F371154852573870068A371154852573870184/  $File/rj371154852573810421.pdf  
[7] Kennedy, D.: Doctor blogs raise concerns about patient privacy,   http://www.npr.org/templates/story/story.php?storyId=88163567  (accessed June 13, 2012)  
[8] Lagu, T., Kaufman, E., Asch, D., Armstrong, K.: Content of Weblogs Written by Health  Professionals. Journal of General Internal Medicine 23(10), 16421646 (2008)  
[9] Li, F., Zou, X., Liu, P., et al.: New threats to health data privacy. BMC Bioinformatics  12, S7 (2011)  
[10] Malik, S., Coulson, N.: Coping with infertility online: an examination of self-help  mechanisms in an online infertility support group. Patient Educ. Couns 81, 315318  (2010)  
[11] McLernon, D.J., Bond, C.M., Hannaford, P.C., Watson, M.C., Lee, A.J., Hazell, L.,  Avery, A.: Adverse drug reaction reporting in the UK: a retrospective observational  comparison of Yellow Card reports submitted by patients and healthcare professionals.  Drug Saf. 33(9), 775788 (2010)  
[12] MedDRA Maintenance and Support Services Organization,   http://www.meddramsso.com (accessed January 1, 2013)   
[13] Miller, A.R., Tucker, C.: Privacy protection and technology adoption: The case of  electronic medical records. Management Science 55(7), 10771093 (2009)  
[14] Renahy, E.: Recherche bdinfomation en matiere de sante sur INternet: determinants,  practiques et impact sur la sante et le recours aux soins, Paris 6 (2008)   
[15] Scanfeld, D., Scanfeld, V., Larson, E.: Dissemination of health information through  social networks: Twitter and antibiotics. American Journal of Infection Control 38(3),  182188 (2010)  
[16] Shani, G., Chickering, D.M., Meek, C.: Mining recommendations from the web.   In: RecSys 2008: Proceedings of the 2008 ACM Conference on Recommender Systems,  pp. 3542 (2008)     Detecting Health-Related Privacy Leaks in Social Networks Using Text Mining Tools 39  
[17] Silverman, E.: Doctor Blogs Reveal Patient Info & Endorse Products. Pharmalot (2008),  http://www.pharmalot.com/2008/07/  doctor-blogs-reveal-patient-info-endorse-products/  (December 15, 2009)   
[18] Systematized Nomenclature of Medicine, http://www.ihtsdo.org/snomed-ct/  (accessed January 1, 2013)   
[19] Sokolova, M., Schramm, D.: Building a patient-based ontology for mining user-written  content. In: Recent Advances in Natural Language Processing, pp. 758763 (2011)  
[20] Star, K., Norn, G.N., Nordin, K., Edwards, I.R.: Suspected adverse drug reaction  reported for children worldwide: an exploratory study using VigiBase. Drug Saf. 34,  415428 (2011)  
[21] Yeniterzi, R., Aberdeen, J., Bayer, S., Wellner, B., Clark, C., Hirschman, L., Malin, B.:  Effects of personal identifier resynthesis on clinical text de-identification. J. Am. Med.  Inform. Assoc. 17(2), 159168 (2010)  
[22] Yu, F.: High Speed Deep Packet Inspection with Hardware Support- Technical Report  No. UCB/EECS-2006-156 (2006),   http://www.eecs.berkeley.edu/Pubs/TechRpts/2006/  EECS-2006-156.html   
[23] Zhang, W., Gunter, C.A., Liebovitz, D., Tian, J., Malin, B.: Role prediction   using electronic medical record system audits. In: AMIA 2011 Annual Symposium,   pp. 858867. American Medical Informatics Association (2011)      
-----2
1. Burch, N., Holte, R.C.: Automatic move pruning revisted. In: Proceedings of the 5th Symposium on Combinatorial Search, SoCS (2012) 
2. Burch, N., Holte, R.C.: Automatic move pruning in general single-player games. In: Proceedings of the 4th Symposium on Combinatorial Search, SoCS (2011) 
3. Taylor, L.A.: Pruning duplicate nodes in depth-first search. Technical Report CSD- 920049, UCLA Computer Science Department (1992) 
4. Taylor, L.A., Korf, R.E.: Pruning duplicate nodes in depth-first search. In: AAAI, pp. 756761 (1993) 
5. Reinefeld, A., Marsland, T.A.: Enhanced iterative-deepening search. IEEE Trans.Pattern Anal. Mach. Intell. 16(7), 701710 (1994) 
6. Korf, R.E.: Finding optimal solutions to Rubiks Cube using pattern databases. In: AAAI, pp. 700705 (1997) 
7. Helmert, M., Lasinger, H.: The scanalyzer domain: Greenhouse logistics as a plan- ning problem. In: ICAPS, pp. 234237 (2010) 
-----2
1. Aiello, L.C., Massacci, F.: Planning attacks to security protocols: Case studies in logic pro- gramming. In: Kakas, A.C., Sadri, F. (eds.) Computat. Logic (Kowalski Festschrift). LNCS (LNAI), vol. 2407, pp. 533560. Springer, Heidelberg (2002) 
2. Armando, A., Compagna, L., Lierler, Y.: Automatic Compilation of Protocol Insecurity Prob- lems into Logic Programming. In: Proceedings of JELIA, pp. 617627 (2004) 
3. Brackin, S., Meadows, C., Millen, J.: CAPSL Interface for the NRL Protocol Analyzer. In: Proceedings of ASSET 1999. IEEE Press (1999) 
4. Burrows, M., Abadi, M., Needham, R.: A logic of authentication. ACM Transactions on Computer Systems 8(1), 1836 (1990) 
5. Dolev, D., Yao, A.C.: On the Security of Public Key Protocols. IEEE Trans. on Inf. The- ory 2(29), 198208 (1983) 
6. Fagin, R., Halpern, J.Y., Moses, Y., Vardi, M.Y.: Reasoning about Knowledge. The MIT Press (1995) 
7. Halpern, J.Y., Pucella, R.: On the Relationship between Strand Spaces and Multi-Agent Sys- tems, CoRR, cs.CR/0306107 (2003) 
8. Hernandez-Orallo, J., Pinto, J.: Especificacion formal de protocolos criptograficos en Calculo de Situaciones. Novatica 143, 5763 (2000) 
9. Hunter, A., Delgrande, J.: Belief Change and Cryptographic Protocol Verification. In: Pro- ceedings of AAAI (2007) 
10. Levesque, H.J., Pirri, F., Reiter, R.: Foundations for the Situation Calculus. Linkoping Elec- tronic Articles in Computer and Information Science 3(18) (1998) 
11. Levesque, H.J., Raymond, R., Lesperance, Y., Lin, F., Scherl, R.: GOLOG: A Logic Program- ming Language for Dynamic Domains. Journal of Logic Programming (31), 13 (1997) 
12. Lowe, G.: Breaking and Fixing the Needham-Schroeder Public-Key Protocol Using FDR.In: Margaria, T., Steffen, B. (eds.) TACAS 1996. LNCS, vol. 1055, pp. 147166. Springer, Heidelberg (1996) 
13. Thayer, J., Herzog, J., Guttman, J.: Strand Spaces: Proving Security Protocols Correct. Jour- nal of Computer Security 7(2-3), 191230 (1999) 
-----2
1. Apache Software Foundation: Apache OpenNLP (2012) 
2. Charniak, E., Johnson, M.: Coarse-to-fine n-best parsing and MaxEnt discrim- inative reranking. In: Proceedings of the 43rd Annual Meeting of the ACL, pp. 173180 (2005) 
3. Collins, M.: Head-driven statistical models for natural language parsing. PhD the- sis, University of Pennsylvania (1999) 
4. Collins, M., Duffy, N.: Convolution kernels for natural language. In: Advances in Neural Information Processing Systems 14, pp. 625632. MIT Press (2001) 
5. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press (2000) 
6. Elwell, R., Baldridge, J.: Discourse connective argument identification with con- nective specific rankers. In: Proceedings of the 2nd IEEE International Conference on Semantic Computing, pp. 198205 (2008) 
7. Ibn Faiz, M.S.: Discovering higher order relations from biomedical text. Masters thesis, The University of Western Ontario, London, Ontario, Canada (2012) 
8. Joachims, T.: Making large-scale support vector machine learning practical. In: Scholkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods: Support Vector Learning, pp. 169184. MIT Press (1999) 
9. Knott, A.: A Data-Driven Methodology for Motivating a Set of Coherence Rela- tions. PhD thesis, University of Edinburgh, Edinburgh (1996) 
10. Lin, Z., Ng, H.T., Kan, M.Y.: A PDTB-styled end-to-end discourse parser. CoRR.Volume arXiv:1011.0835 (2010) 
11. McClosky, D., Charniak, E., Johnson, M.: Automatic domain adaptation for pars- ing. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the ACL (HLT 2010), pp. 2836 (2010) 76 S.I. Faiz and R.E. Mercer 
12. Moschitti, A.: Making tree kernels practical for natural language learning. In: Pro- ceedings of the 11th Conference of the European Chapter of the ACL (EACL 2006), pp. 113120 (2006) 
13. Pitler, E., Nenkova, A.: Using syntax to disambiguate explicit discourse connec- tives in text. In: Proceedings of the ACL-IJCNLP 2009 Conference Short Papers (ACLShort 2009), pp. 1316 (2009) 
14. Prasad, R., Dinesh, N., Lee, A., Miltsakaki, E., Robaldo, L., Joshi, A.K., Webber, B.L.: The Penn Discourse TreeBank 2.0. In: Proceedings of the 6th International Conference on Language Resources and Evaluation, LREC 2008 (2008) 
15. Prasad, R., McRoy, S., Frid, N., Joshi, A., Yu, H.: The biomedical discourse relation bank. BMC Bioinformatics 12(1), 188205 (2011) 
16. Ramesh, B.P.P., Yu, H.: Identifying discourse connectives in biomedical text.In: Proceedings of the American Medical Informatics Association Fall Symposium (AIMA 2010), pp. 657661 (2010) 
17. Ratnaparkhi, A.: Maximum entropy models for natural language ambiguity reso- lution. PhD thesis, University of Pennsylvania (1998) 
18. Refaeilzadeh, P., Tang, L., Liu, H.: Cross-Validation. In: Liu, L., Ozsu, M.T. (eds.) Encyclopedia of Database Systems, pp. 532538. Springer (2009) 
19. Roark, B., Mitchell, M., Hollingshead, K.: Syntactic complexity measures for detecting Mild Cognitive Impairment. In: Biological, Translational, and Clinical Language Processing, pp. 18. Association for Computational Linguistics (2007) 
20. Settles, B.: Biomedical named entity recognition using conditional random fields and rich feature sets. In: Proceedings of the International Joint Workshop on Nat- ural Language Processing in Biomedicine and its Applications (JNLPBA 2004), pp. 104107 (2004) 
21. Settles, B.: ABNER: An open source tool for automatically tagging genes, proteins, and other entity names in text. Bioinformatics 21(14), 31913192 (2005) 
22. Sutton, C., McCallum, A.: An introduction to conditional random fields for rela- tional learning. In: Getoor, L., Taskar, B. (eds.) Introduction to Statistical Rela- tional Learning. MIT Press (2007) 
23. Vapnik, V.N.: Statistical learning theory. Wiley (1998) 
24. Wellner, B.: Sequence models and ranking methods for discourse parsing. PhD thesis, Brandeis University, Waltham, MA, USA (2009) 
25. Wilcoxon, F.: Individual Comparisons by Ranking Methods. Biometrics Bul- letin 1(6), 8083 (1945) 
26. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Tech- niques with Java Implementations, 1st edn. Morgan Kaufmann (1999) 
-----2
1. American Medical Association: Preparing for the icd-10 code set (2010), http://www.ama-assn.org/ama1/pub/upload/mm/ 399/icd10-icd9-differences-fact-sheet.pdf 
2. National Center for Health Statistics and the Centers for Medicare and Medicaid Services (2011), http://www.cdc.gov/nchs/icd/icd9cm.htm 
3. de Lima, L.R.S., Laender, A.H.F., Ribeiro-Neto, B.A.: A hierarchical approach to the automatic categorization of medical documents. In: Proceedings of the 7th Iintl. Conf. on Inf. & Knowledge Mgmt., CIKM 1998, pp. 132139 (1998) 
4. Gundersen, M.L., Haug, P.J., Pryor, T.A., van Bree, R., Koehler, S., Bauer, K., Clemons, B.: Development and evaluation of a computerized admission diagnosis encoding system. Comput. Biomed. Res. 29(5), 351372 (1996) 
5. Pestian, J.P., Brew, C., Matykiewicz, P., Hovermale, D.J., Johnson, N., Cohen, K.B., Duch, W.: A shared task involving multi-label classification of clinical free text. In: Proceedings of the Workshop on BioNLP 2007, pp. 97104 (2007) 
6. Aronson, A.R., Bodenreider, O., Demner-Fushman, D., Fung, K.W., Lee, V.K., Mork, J.G., Neveol, A., Peters, L., Rogers, W.J.: From indexing the biomed- ical literature to coding clinical text: experience with mti and machine learn- ing approaches. In: Biological, Translational, and Clinical Language Processing, pp. 105112. Assc. for Comp. Ling. (2007) 
7. Goldstein, I., Arzumtsyan, A., Uzuner, O.: Three approaches to automatic assign- ment of icd-9-cm codes to radiology reports. In: Proceedings of AMIA Symposium, pp. 279283 (2007) 
8. Crammer, K., Dredze, M., Ganchev, K., Pratim Talukdar, P., Carroll, S.: Auto- matic code assignment to medical text. In: Biological, Translational, and Clinical Language Processing, pp. 129136. Assc. for Comp. Ling. (2007) 
9. Farkas, R., Szarvas, G.: Automatic construction of rule-based icd-9-cm coding sys- tems. BMC Bioinformatics 9(S-3) (2008) 
10. Pakhomov, S.V.S., Buntrock, J.D., Chute, C.G.: Automating the assignment of diagnosis codes to patient encounters using example-based and machine learning techniques. J. American Medical Informatics Assoc. 13(5), 516525 (2006) 
11. Nadeau, D., Sekine, S.: A survey of named entity recognition and classification.Lingvisticae Investigationes 30(1), 326 (2007) 
12. Aronson, A.R., Lang, F.M.: An overview of metamap: historical perspective and recent advances. J. American Medical Informatics Assoc. 17(3), 229236 (2010) 
13. Frantzi, K.T., Ananiadou, S., Tsujii, J.: The C ? value/NC ? value Method of Automatic Recognition for Multi-word Terms. In: Nikolaou, C., Stephanidis, C.(eds.) ECDL 1998. LNCS, vol. 1513, pp. 585604. Springer, Heidelberg (1998) 
14. Bodenreider, O., Nelson, S., Hole, W., Chang, H.: Beyond synonymy: exploiting the umls semantics in mapping vocabularies. In: Proceedings of AMIA Symposium, pp. 815819 (1998) 
15. Mihalcea, R., Tarau, P.: Textrank: Bringing order into text. In: Proceedings of EMNLP, pp. 404411 (2004) 
16. Tsoumakas, G., Katakis, I., Vlahavas, I.P.: Mining multi-label data. In: Data Min- ing and Knowledge Discovery Handbook, pp. 667685 (2010) 
-----2
1. Buffett, S., Fleming, M.: Persistently Effective Query Selection in Preference Elicitation.  In: The 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology  (IAT 2007), Fremont, California, USA, pp. 491497 (2007)  
2. Chen, S., Buffett, S., Fleming, M.W.: Reasoning with conditional preferences across  attributes. In: Kobti, Z., Wu, D. (eds.) Canadian AI 2007. LNCS (LNAI), vol. 4509,   pp. 369380. Springer, Heidelberg (2007)  
3. Miguel, F., Ryan, M., Scott, A.: Are preferences stable? The case of health care. Journal of  Economic Behavior & Organization 48, 114 (2002)  
4. Koopmans, T.: On flexibility of future preference. In: Cowles Foundation Discussion   Papers (1962)  
5. Pu, P., Faltings, B., Torrens, M.: Effective interaction principles for on-line product search  environments. In Proceedings of the 3rd ACM/IEEE International Conference on Web   intelligence. IEEE Press (2004)   
6. Boutilier, C., Brafman, R., Domshlak, C., Hoos, H., Poole, D.: CP-nets: A Tool for  Representing and Reasoning with Conditional Ceteris Paribus Preference Statements.  Journal of Artificial Intelligence Research 21, 135191 (2004)  
7. La Poutr, J.A., Leeuwen, J.V.: Maintenance of transitive closures and transitive reduc- tions of graphs (1988)  
8. King, V., Sagert, G.: A Fully Dynamic Algorithm for Maintaining the Transitive Closure.  Journal of Computer and System Sciences 65, 150167 (2002)  
9. Chajewska, U., Koller, D., Parr, R.: Making rational decisions using adaptive utility elici- tation. In: AAAI 2000, Austin, Texas, USA, pp. 363369 (2000)  
10. Boutilier, C., Regan, K., Viappiani, P.: Simultaneous Elicitation of Preference Features  and Utility. In: Proceedings of the Twenty-fourth AAAI Conference on Artificial Intelli- gence (AAAI 2010), Atlanta, GA, pp. 11601167 (2010)  
11. Guo, S., Sanner, S.: Real-time Multiattribute Bayesian Preference Elicitation with Pairwise  Comparison Queries. Appearing in Proceedings of the 13th International Conference on  Artificial Intelligence and Statistics (AISTATS), Chia Laguna Resort, Sardinia, Italy.  JMLR: W&CP, vol. 9 (2010)  
12. Kaci, S.: Working with Preferences: Less Is More. Cognitive Technologies, pp. 11193.  Springer (2011)  
-----2
1. Bjornsson, Y., Bulitko, V., Sturtevant, N.: TBA*: Time-bounded A*. In: Proceedings of the International Joint Conferences on Artificial Intelligence (IJCAI), pp. 431436 (2009) 
2. Botea, A.: Ultra-fast optimal pathfinding without runtime search. In: Proceedings of the Second Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE), pp. 122127 (2011) 
3. Bulitko, V., Bjornsson, Y., Lawrence, R.: Case-based subgoaling in real-time heuristic search for video game pathfinding. Journal of Artificial Intelligence Research 39, 269300 (2010) 
4. Bulitko, V., Lus?trek, M., Schaeffer, J., Bjornsson, Y., Sigmundarson, S.: Dynamic control in real-time heuristic search. Journal of Artificial Intelligence Research 32, 419452 (2008) 
5. Hart, P., Nilsson, N., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics 4(2), 100107 (1968) 
6. Hernandez, C., Baier, J.A.: Fast subgoaling for pathfinding via real-time search. In: Bacchus, F., Domshlak, C., Edelkamp, S., Helmert, M. (eds.) Proceedings of the International Confer- ence on Artificial Intelligence Planning Systems (ICAPS), pp. 327330. AAAI (2011) 
7. Hernandez, C., Baier, J.A.: Real-time heuristic search with depression avoidance. In: Pro- ceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 578583 (2011) 
8. Lawrence, R., Bulitko, V.: Database-driven real-time heuristic search in video-game pathfinding. IEEE Transactions on Computer Intelligence and AI in Games PP(99) (2013) 
9. Sturtevant, N.: Memory-efficient abstractions for pathfinding. In: Proceedings of Artificial Intelligence and Interactive Digital Entertainment (AIIDE), pp. 3136 (2007) 
10. Sturtevant, N.R.: Benchmarks for grid-based pathfinding. IEEE Transactions on Computer Intelligence and AI in Games 4(2), 144148 (2012) 
-----2
1. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB 1994, pp. 487499 (1994) 
2. Chiu, B., Keogh, E., Lonardi, S.: Probabilistic discovery of time series motifs. In: KDD 2003, pp. 493498 (2003) 
3. Laxman, S., Sastry, P., Unnikrishnan, K.: A fast algorithm for finding frequent episodes in event streams. In: KDD 2007, pp. 410419 (2007) 
4. Mannila, H., Toivonen, H., Verkamo, A.: Discovering frequent episodes in se- quences. In: KDD 1995, pp. 210215 (1995) 
5. Meester, R.: A Natural Introduction to Probability Theory (2004) 
6. Minnen, D., Isbell, C., Essa, I.: Discovering multivariate motifs using subsequence density estimation and greedy mixture learning. In: AAAI 2007, pp. 615620 (2007) 
7. Minnen, D., Starner, T., Essa, I., Isbell, C.: Discovering characteristic actions from on-body sensor data. In: ISWC 2006, pp. 1118 (2006) 
8. Minnen, D., Starner, T., Essa, I., Isbell, C.: Improving activity discovery with automatic neighborhood estimation. In: IJCAI 2007, pp. 28142819 (2007) 
9. Patnaik, D., Laxman, S.: Discovering excitatory networks from discrete event streams with applications to neuronal spike train analysis. In: ICDM 2009, pp.407416 (2009) 
10. Raajay, V.: Frequent episode mining and multi-neuronal spike train data analysis.Masters thesis, IISc, Bangalore (2009) 
11. Sastry, P., Unnikrishnan, K.: Conditional probability-based significance tests for sequential patterns in multineuronal spike trains. Neural Computation (2010) 
12. Shimazaki, H., Shinomoto, S.: A method for selecting the bin size of a time his- togram. Neural Computation 19(6), 15031527 (2007) 
13. Tanaka, Y., Iwamoto, K., Uehara, K.: Discovery of time-series motif from multi- dimensional data based on mdl principle. Machine Learning 58, 269300 (2005) 
14. Vahdatpour, A., Amini, N.: Toward unsupervised activity discovery using multi- dimensional motif detection in time series. In: IJCAI 2009, pp. 12611266 (2009) 
15. Wu, W., Au, L., Jordan, B.: The smartcane system: an assistive device for geri- atrics. In: BodyNets 2008, pp. 14 (2008) 
-----2
1. Angelova, R., Weikum, G.: Graph-based text classification: learn from your neigh- bors. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2006, pp. 485492.ACM (2006) 
2. Baldi, P., Frasconi, P., Smyth, P.: Modeling the Internet and the Web: probabilistic methods and algorithms. Wiley Series in Probability and Statistics. Wiley (2003) 
3. Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., Hullen- der, G.: Learning to rank using gradient descent. In: Proceedings of the 22nd In- ternational Conference on Machine Learning, ICML 2005, pp. 8996. ACM (2005) 
4. Carlson, A., Schafer, C.: Bootstrapping information extraction from semi- structured web pages. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part I. LNCS (LNAI), vol. 5211, pp. 195210. Springer, Heidelberg (2008) 
5. Chakrabarti, S., Dom, B., Indyk, P.: Enhanced hypertext categorization using hy- perlinks. SIGMOD Rec. 27(2), 307318 (1998) 
6. Chakrabarti, S., van den Berg, M., Dom, B.: Focused crawling: a new approach to topic-specific web resource discovery. Comput. Netw. 31(11-16), 16231640 (1999) 
7. Freund, Y., Iyer, R., Schapire, R.E., Singer, Y.: An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res. 4, 933969 (2003) 
8. Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classifi- cation using support vector machines. Mach. Learn. 46(1-3), 389422 (2002) 
9. Hao, Q., Cai, R., Pang, Y., Zhang, L.: From one tree to a forest: a unified solution for structured web data extraction. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, pp. 775784. ACM (2011) 
10. Herbrich, R., Graepel, T., Obermayer, K.: Support vector learning for ordinal re- gression. In: International Conference on Artificial Neural Networks, pp. 97102 (1999) Selective Retrieval for Categorization of Semi-structured Web Resources 137 
11. Joachims, T.: Training linear SVMs in linear time. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Min- ing, KDD 2006, pp. 217226. ACM (2006) 
12. McCallum, A., Nigam, K.: A comparison of event models for naive bayes text classification. In: AAAI 1998 Workshop on Learning For Text Categorization, pp. 4148. AAAI Press (1998) 
13. Nguyen, H., Fuxman, A., Paparizos, S., Freire, J., Agrawal, R.: Synthesizing prod- ucts for online catalogs. Proc. VLDB Endow. 4(7), 409418 (2011) 
14. Oh, H.-J., Myaeng, S.H., Lee, M.-H.: A practical hypertext categorization method using links and incrementally available class information. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2000, pp. 264271. ACM (2000) 
15. Pal, A., Tomar, D.S., Shrivastava, S.C.: Effective focused crawling based on con- tent and link structure analysis. International Journal of Computer Science and Information Security 2(1), 140152 (2009) 
16. Pant, G., Srinivasan, P.: Learning to crawl: Comparing classification schemes. ACM Trans. Inf. Syst. 23(4), 430462 (2005) 
17. Qi, X., Davison, B.D.: Web page classification: Features and algorithms. ACM Comput. Surv. 41(2), 12:112:31 (2009) 
18. Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. 85(3), 333359 (2011) 
19. Roth, S.P., Schmutz, P., Pauwels, S.L., Bargas-Avila, J.A., Opwis, K.: Mental models for web objects: Where do users expect to find the most frequent objects in online shops, news portals, and company web pages? Interact. Comput. 22(2), 140152 (2010) 
20. Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge.In: Proceedings of the 16th International Conference on World Wide Web, WWW 2007, pp. 697706. ACM (2007) 
21. Tang, T.T., Hawking, D., Craswell, N., Griffiths, K.: Focused crawling for both top- ical relevance and quality of medical information. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, CIKM 2005, pp. 147154. ACM (2005) 
22. Tsoumakas, G., Vlahavas, I.: Random k-labelsets: An ensemble method for multi- label classification. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenic?, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 406417. Springer, Heidelberg (2007) 
23. Xu, J., Li, H.: Adarank: a boosting algorithm for information retrieval. In: Pro- ceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2007, pp. 391398. ACM (2007) 
-----2
1. Hafting, T., Fyhn, M., Molden, S., Moser, M., Moser, E.: Microstructure of a spatial map in the entorhinal cortex. Nature 436(7052), 801806 (2005) 
2. Welday, A.C., Shlifer, I.G., Bloom, M.L., Zhang, K., Blair, H.T.: Cosine Directional Tuning of Theta Cell Burst Frequencies: Evidence for Spatial Coding by Oscillatory Interference. Journal of Neuroscience 31(45), 1615716176 (2011) 
3. Moser, E., Kropff, E., Moser, M.: Place cells, grid cells, and the brains spatial representation system. Annu. Rev. Neurosci. 31, 6989 (2008) 
4. Blair, H., Gupta, K., Zhang, K.: Conversion of a phase- to a rate-coded position signal by a three-stage model of theta cells, grid cells, and place cells. Hippocam- pus 18(12), 12391255 (2008) Navigation by Path Integration and the Fourier Transform 149 
5. Burgess, N., Barry, C., OKeefe, J.: An oscillatory interference model of grid cell firing. Hippocampus 17(9), 801812 (2007) 
6. Krupic, J., Burgess, N., OKeefe, J.: Neural Representations of Location Composed of Spatially Periodic Bands. Science 337(6096), 853857 (2012) 
7. Fuhs, M.C., Touretzky, D.S.: A spin glass model of path integration in rat medial entorhinal cortex. The Journal of Neuroscience 26(16), 42664276 (2006) 
8. Solstad, T., Moser, E., Einevoll, G.T.: From grid cells to place cells: a mathematical model. Hippocampus 16(12), 10261031 (2006) 
9. OKeefe, J., Burgess, N.: Dual phase and rate coding in hippocampal place cells: theoretical significance and relationship to entorhinal grid cells. Hippocam- pus 15(7), 853866 (2005) 
10. McNaughton, B., Battaglia, F.P., Jensen, O., Moser, E., Moser, M.: Path integra- tion and the neural basis of the cognitive map. Nature Reviews Neuroscience 7(8), 663678 (2006) 
11. Blair, H., Welday, A.C., Zhang, K.: Scale-Invariant Memory Representations Emerge from Moire Interference between Grid Fields That Produce Theta Oscilla- tions: A Computational Model. Journal of Neuroscience 27(12), 32113229 (2007) 
12. Zilli, E.A.: Models of grid cell spatial firing published 20052011. Frontiers in Neu- ral Circuits 6(16), 117 (2012) 
13. Conklin, J., Eliasmith, C.: A Controlled Attractor Network Model of Path Inte- gration in the Rat. Journal of Computational Neuroscience 18, 183203 (2005) 
14. Koch, C.: Simplified models of individual neurons. Biophysics of computation (1999) 
15. Eliasmith, C., Anderson, C.H.: Neural engineering: Computation, representation, and dynamics in neurobiological systems. MIT Press, Cambridge (2003) 
16. Eliasmith, C., Stewart, T.C., Choo, X., Bekolay, T., DeWolf, T., Tang, C., Ras- mussen, D.: A Large-Scale Model of the Functioning Brain. Science 338(6111), 12021205 (2012) 
17. Ji, X., Kushagra, S., Orchard, J.: Sensory updates to combat path-integration drift. In: ZaIane, O., Zilles, S. (eds.) Canadian AI 2013. LNCS (LNAI), vol. 7884, pp. 263270. Springer, Heidelberg (2013) 
18. Mathis, A., Herz, A.: Optimal Population Codes for Space: Grid Cells Outperform Place Cells. Neural Computation 24, 22802317 (2012) 
19. Rolls, E.T., Stringer, S.M., Elliot, T.: Entorhinal cortex grid cells can map to hippocampal place cells by competitive learning. Network: Computation in Neural Systems 17(4), 447465 (2006) 
20. de Almeida, L., Idiart, M., Lisman, J.E.: The inputoutput transformation of the hippocampal granule cells: from grid cells to place fields. The Journal of Neuro- science 29(23), 75047512 (2009) 
-----0
AbdelRahman, S., Blake, C.: Sbdlrhmn: A Rule-based Human Interpretation System for Semantic Textual Similarity Task. In: Proceedings of the 6th International Workshop on Semantic Evaluation (SemEval 2012), in Conjunction with the First Joint Conference on Lexical and Computational Semantics (2012) 
Agirre, E., Cer, D., Diab, M., Gonzalez-Agirre, A.: Semeval-2012 Task 6: A Pilot on Semantic Textual Similarity. In: Proceedings of the 6th International Workshop on Semantic Evaluation (SemEval 2012), in Conjunction with the First Joint Conference on Lexical and Computational Semantics (2012) 
Allison, L., Dix, T.I.: A Bit-String Longest-Common-Subsequence Algorithm. Information Processing Letters 23(5) (1986) 
Banea, C., Hassan, S., Mohler, M., Mihalcea, R.: UNT: A Supervised Synergistic Approach to Semantic Text Similarity. In: Proceedings of the 6th International Workshop on Semantic Evaluation (SemEval 2012), in Conjunction with the First Joint Conference on Lexical and Computational Semantics (2012) 
Banerjee, S., Pedersen, T.: Extended Gloss Overlaps as a Measure of Semantic Relatedness. In: International Joint Conference on Artificial Intelligence, vol. 18. Lawrence Erlbaum Associates Ltd. (2003) 
Bar, D., Biemann, C., Gurevych, I., Zesch, T.: UKP: Computing Semantic Textual Similarity by Combining Multiple Content Similarity Measures. In: Proceedings of the 6th International Workshop on Semantic Evaluation (SemEval 2012), in Conjunction with the First Joint Conference on Lexical and Computational Semantics (2012) 160 E. Shareghi and S. Bergler 
Budanitsky, A., Hirst, G.: Evaluating WordNet-based Measures of Lexical Semantic Relatedness. Computational Linguistics 32(1) (2006) 
Chan, P.K., Stolfo, S.J.: A comparative evaluation of voting and meta-learning on partitioned data. In: Machine Learning International Conference. Citeseer (1995) 
Cohen, W.W., Ravikumar, P., Fienberg, S.E., et al.: A Comparison of String Distance Metrics for Name-Matching Tasks. In: Proceedings of the International Joint Conference on Artificial Intelligence Workshop on Information Integration on the Web, IIWeb 2003 (2003) 
Dagan, I., Glickman, O., Magnini, B.: The Pascal Recognising Textual Entailment Challenge. In: Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment (2006) 
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by Latent Semantic Analysis. Journal of the American society for information science 41(6) (1990) 
Dolan, B., Quirk, C., Brockett, C.: Unsupervised Construction of Large Paraphrase Corpora: Exploiting Massively Parallel News Sources. In: Proceedings of the 20th International Conference on Computational Linguistics. Association for Computational Linguistics (2004) 
Dunn, O.J., Clark, V.: Correlation coefficients measured on the same individuals. Journal of the American Statistical Association 64(325) (1969) Fellbaum, C.: WordNet. Theory and Applications of Ontology: Computer Applications (2010) 
Gabrilovich, E., Markovitch, S.: Computing Semantic Relatedness Using Wikipediabased Explicit Semantic Analysis. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence (2007) 
Gusfield, D.: Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology. Cambridge Univ. Press (1997) 
Guthrie, D., Allison, B., Liu, W., Guthrie, L., Wilks, Y.: A Closer Look at SkipGram Modelling. In: Proceedings of the 5th International Conference on Language Resources and Evaluation (2006) 
Guyon, I., Elisseeff, A.: An Introduction to Variable and Feature Selection. The Journal of Machine Learning Research 3 (2003) 
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: an Update. ACM SIGKDD Explorations Newsletter 11(1) (2009) 
Inkpen, D., Kipp, D., Nastase, V.: Machine Learning Experiments for Textual Entailment. In: Proceedings of the Second Recognizing Textual Entailment Challenge (2006) 
Jarmasz, M., Szpakowicz, S.: Rogets Thesaurus and Semantic Similarity. In: Proceedings of the Conference on Recent Advances in Natural Language Processing (2003) 
Jaro, M.A.: Advances in Record-Linkage Methodology as Applied to Matching the 1985 Census of Tampa, Florida. Journal of the American Statistical Association (1989) 
Jiang, J.J., Conrath, D.W.: Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. In: Proceedings of the 10th International Conference on Research on Computational Linguistics (1997) 
Koehn, P., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., Bertoldi, N., Cowan, B., Shen, W., Moran, C., Zens, R., et al.: Moses: Open Source Toolkit for Statistical Machine Translation. In: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions. Association for Computational Linguistics (2007) Feature Combination for Sentence Similarity 161 
Landauer, T.K., Foltz, P.W., Laham, D.: An Introduction to Latent Semantic Analysis.Discourse Processes 25(2-3) (1998) 
Leacock, C., Chodorow, M.: Combining Local Context and WordNet Similarity for Word Sense Identification. WordNet: An Electronic Lexical Database 49(2) (1998) 
Levenshtein, V.I.: Binary Codes Capable of Correcting Deletions, Insertions, and Reversals. Soviet Physics Doklady 10(8) (1966) Lin, C.Y., Och, F.J.: Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence and Skip-Bigram Statistics. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics (2004) 
Lin, D.: An Information-Theoretic Definition of Similarity. In: Proceedings of the 15th International Conference on Machine Learning, vol. 1 (1998) 
Lin, C.Y.: ROUGE: A Package for Automatic Evaluation of Summaries. In: Text Summarization Branches Out: Proceedings of the ACL 2004 Workshop (2004) 
Manning, C.D., Schutze, H.: Foundations of Statistical Natural Language Processing.MIT Press (1999) 
Mihalcea, R., Corley, C., Strapparava, C.: Corpus-based and Knowledge-based Measures of Text Semantic Similarity. In: Proceedings of the National Conference on Artificial Intelligence (2006) 
Monge, A., Elkan, C.: An Efficient Domain-Independent Algorithm for Detecting Approximately Duplicate Database Records. In: Proceedings of the SIGMODWorkshop on Data Mining and Knowledge Discovery. Citeseer (1997) 
Morris, J., Hirst, G.: Lexical Cohesion Computed by Thesaural Telations as an Indicator of the Structure of Text. Computational Linguistics 17(1) (1991) Ng, A.Y.: On Feature Selection: Learning with Exponentially many Irrelevant Features as Training Examples. In: Proceedings of the 15th International Conference on Machine Learning (1998) 
Patwardhan, S.: Incorporating Dictionary and Corpus Information into a Context Vector Measure of Semantic Relatedness. Masters thesis, University of Minnesota (2003) 
Pedersen, T., Patwardhan, S., Michelizzi, J.: WordNet: Similarity: Measuring the Relatedness of Concepts. In: Demonstration Papers at North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics (2004) 
Resnik, P.: Using Information Content to Evaluate Semantic Similarity in a Taxonomy.In: Proceedings of the 14th International Joint Conference on Artificial Intelligence (1995) 
S?aric, F., Glavas?, G., Karan, M., S?najder, J., Bas?ic, B.D.: TakeLab: Systems for Measuring Semantic Text Similarity. In: Proceedings of the 6th International Workshop on Semantic Evaluation (SemEval 2012), in Conjunction with the First Joint Conference on Lexical and Computational Semantics (2012) 
Smola, A.J., Scholkopf, B.: A Tutorial on Support Vector Regression. Statistics and Computing 14(3) (2004) 
Turney, P.D.: Mining the web for synonyms: PMI-IR versus LSA on TOEFL. In: Flach, P.A., De Raedt, L. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 491 502. Springer, Heidelberg (2001) 
Winkler, W.E.: String Comparator Metrics and Enhanced Decision Rules in the FellegiSunter Model of Record Linkage. In: Proceedings of the Section on Survey Research Methods. American Statistical Association (1990) 
Wu, Z., Palmer, M.: Verbs Semantics and Lexical Selection. In: Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics (1994) 
-----2
1. Bauer, M.: Approximations algorithm and decision making in the Dempster-Shafer theory of evidence - an empirical study. International Journal of Approximate Reasoning 17(2-3), 217237 (1997) 
2. Bosse, E., Jousseleme, A.L., Grenier, D.: A new distance between two bodies of evidence. Information Fusion 2, 91101 (2001) 
3. Deng, D., Huang, H.: A new discernibility matrix and function. In: Wang, G.-Y., Peters, J.F., Skowron, A., Yao, Y. (eds.) RSKT 2006. LNCS (LNAI), vol. 4062, pp. 114121. Springer, Heidelberg (2006) 
4. Elouedi, Z., Mellouli, K., Smets, P.: Assessing sensor reliability for multisensor data fusion within the transferable belief model. IEEE Trans. Syst. Man Cybern. 34(1), 782787 (2004) 
5. Fixen, D., Mahler, R.P.S.: The modified Dempster-Shafer approach to classifica- tion. IEEE Trans. Syst. Man Cybern. 27(1), 96104 (1997) 
6. Hu, X., Cercone, N.: Learning in Relational Databases: a Rough Set Approach.Computational Intelligence 2, 323337 (1995) 
7. Modrzejewski, M.: Feature selection using rough sets theory. In: Proceedings of the 11th International Conference on Machine Learning, pp. 213226 (1993) 
8. Lingras, P., West, C.: Interval Set Clustering of Web Users with Rough K-means.Journal of Intelligent Information Systems 23(1), 516 (2004) 
9. Pawlak, Z.: Rough Sets. International Journal of Computer and Information Sci- ences 11, 341356 (1982) 
10. Pawlak, Z., Zdzislaw, A.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishing, Dordrecht (1991) ISBN 0-7923-1472-7 
11. Pawlak, Z., Rauszer, C.M.: Dependency of attributes in Information systems. Bull.Polish Acad. Sci., Math. 33, 551559 (1985) 
12. Rauszer, C.M.: Reducts in Information systems. Fundamenta Informaticae (1990) 
13. Shafer, G.: A mathematical theory of evidence. Princeton University Press, Prince- ton (1976) 
14. Skowron, A., Rauszer, C.: The Discernibility Matrices and Functions in Infor- ma- tion Systems. In: Slowiski, R. (ed.) Intelligent Decision Support, Handbook of Ap- plications and Advances of the Rough Set Theory, pp. 311362. Kluwer Academic Publishers, Dordrecht (1992) 
15. Skowron, A.: Rough Sets in KDD. Special Invited Speaking, WCC 2000 in Beijing (August 2000) 
16. Smets, P., Kennes, R.: The transferable belief model. Artificial Intelligence 66, 191236 (1994) 
17. Smets, P.: The transferable belief model for quantified belief representation. In: Gabbay, D.M., Smets, P. (eds.) Handbook of Defeasible Reasoning and Uncertainty Management Systems, vol. 1, pp. 207301. Kluwer, Doordrecht (1998) 
18. Trabelsi, S., Elouedi, Z.: Heuristic method for attribute selection from partially uncertain data using rough sets. International Journal of General Systems 39(3), 271290 (2010) Exhaustive Search with Belief Discernibility Matrix and Function 173 
19. Trabelsi, S., Elouedi, Z., Lingras, P.: Heuristic for Attribute Selection Using Belief Discernibility Matrix. In: Li, T., Nguyen, H.S., Wang, G., Grzymala-Busse, J., Janicki, R., Hassanien, A.E., Yu, H. (eds.) RSKT 2012. LNCS (LNAI), vol. 7414, pp. 129138. Springer, Heidelberg (2012) 
20. Trabelsi, S., Elouedi, Z., Lingras, P.: Belief Rough Set Classifier. In: Gao, Y., Japkowicz, N. (eds.) AI 2009. LNCS (LNAI), vol. 5549, pp. 257261. Springer, Heidelberg (2009) 
21. Yao, Y., Zhao, Y.: Discernibility Matrix Simplification for Constructing Attribute Reducts. Information Sciences 179(5), 867882 (2009) 
-----2
1. Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Machine  Learning: Proceedings of the Thirteenth International Conference, pp. 148156 (1996)  
2. Kubat, M., Matwin, S.: Addressing the curse of imbalanced training sets: One-sided  selection. In: Proceddings of the Fourteenth International Conference on Machine  Learning, pp. 179186 (1997)  
3. Dietterich, T., Lathrop, R., Lozano-Perez, T.: Solving the multiple instance problem with  the axis-parallel rectangles. Artificial Intelligence 89(1-2), 3171 (1997)  
4. Maron, O., Lozano-Prez, T.: A framework for multiple instance learning. In: Proc. of the  1997 Conf. on Advances in Neural Information Processing Systems 10, pp. 570576  (1998)  
5. Fan, W., Stolfo, S.J., Zhang, J., Chan, P.K.: AdaCost: Misclassification Cost-Sensitive  Boosting. In: Proc. Intl Conf. Machine Learning, pp. 97105 (1999)  
6. Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated  predictions. Machine Learning 37(3), 297336 (1999)  
7. Ting, K.M.: A Comparative Study of Cost-Sensitive Boosting Algorithms. In: Proc. Intl  Conf. Machine Learning, pp. 983990 (2000)  
8. Wang, J., Zucker, J.D.: Solving the multiple-instance problem: A lazy learning approach.  In: ICML (2000)  
9. Japkowicz, N.: Learning from Imbalanced Data Sets: A Comparison of Various Strategies.  In: Proc. Am. Assoc. for Artificial Intelligence (AAAI) Workshop Learning from  Imbalanced Data Sets, pp. 10-15 (Technical Report WS-00-05) (2000)  
10. Zhang, Q., Goldman, S.A.: EM-DD: An improved multiple instance learning technique.  In: Neural Information Processing Systems 14 (2001)   
11. Elkan, C.: The Foundations of Cost-Sensitive Learning. In: Proc. Intl Joint Conf.  Artificial Intelligence, pp. 973978 (2001)  
12. Ting, K.M.: An Instance-Weighting Method to Induce Cost-Sensitive Trees. IEEE Trans.  Knowledge and Data Eng. 14(3), 659665 (2002)  
13. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: Synthetic Minority  Over-sampling Technique. Journal of Artificial Intelligence Research 16, 321357 (2002)  
14. Zhang, M.L., Goldman, S.: Em-dd: An improved multi-instance learning technique.   In: NIPS (2002)  
15. Andrews, S., Tsochandaridis, I., Hofman, T.: Support vector machines for multiple  instance learning. Adv. Neural. Inf. Process. Syst. 15, 561568 (2003)  
16. Batista, G.E.A.P.A., Prati, R.C., Monard, M.C.: A Study of the Behavior of Several  Methods for Balancing Machine Learning Training Data. ACM SIGKDD Explorations  Newsletter 6(1), 2029 (2004)  
17. Blockeel, H., Page, D., Srinivasan, A.: Multi-instance tree learning. In: ICML (2005)  
18. Sun, Y., Kamel, M.S., Wong, A.K.C., Wang, Y.: Cost-Sensitive Boosting for  Classification of Imbalanced Data. Pattern Recognition 40(12), 33583378 (2007)  186 X. Wang et al.  
19. Foulds, J., Frank, E.: Revisiting multiple-instance learning via embedded instance  selection. In: Wobcke, W., Zhang, M. (eds.) 21st Australasian Joint Conference on  Artificial Intelligence Auckland, New Zealand, pp. 300310 (2008)  
20. Leistner, C., Saffari, A., Bischof, H.: MIForests: Multiple-instance learning with  randomized trees. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI.  LNCS, vol. 6316, pp. 2942. Springer, Heidelberg (2010)  
21. Bjerring, L., Frank, E.: Beyond trees: Adopting MITI to learn rules and ensemble  classifiers for multi-instance data. In: Wang, D., Reynolds, M. (eds.) AI 2011. LNCS  (LNAI), vol. 7106, pp. 4150. Springer, Heidelberg (2011)  
22. Japkowicz, N., Shah, M.: Evaluating Learning Algorithms: A Classification Perspective.  Cambridge University Press (2011)  
23. Wang, X., Shao, H., Japkowicz, N., Matwin, S., Liu, X., Bourque, A., Nguyen, B.: Using  SVM with Adaptively Asymmetric Misclassification Costs for Mine-Like Objects  Detection. In: ICMLA (2012)  
-----2
1. Brito, I., Meseguer, P.: Cluster tree elimination for distributed constraint optimization with quality guarantees. Fundamenta Informaticae 102(3-4), 263286 (2010) 
2. Gmytrasiewicz, P., Durfee, E.: Rational communication in multi-agent environments. Auto.Agents and Multi-Agent Systems 4(3), 233272 (2001) 
3. Koller, D., Milch, B.: Multi-agent influence diagrams for representing and solving games.In: Proc. 17th Inter. Joint Conf. on Artificial Intelligence, pp. 10271034 (2001) 
4. Maestre, A., Bessiere, C.: Improving asynchronous backtracking for dealing with complex local problems. In: Proc. 16th European Conf. on Artificial Intelligence, pp. 206210 (2004) 
5. Modi, P., Shen, W., Tambe, M., Yokoo, M.: Adopt: asynchronous distributed constraint opti- mization with quality guarantees. Artificial Intelligences 161(1-2), 149180 (2005) 
6. Paskin, M., Guestrin, C., McFadden, J.: A robust architecture for distributed inference in sensor networks. In: Proc. Information Processing in Sensor Networks, pp. 5562 (2005) 
7. Petcu, A., Faltings, B.: A scalable method for multiagent constraint optimization. In: Proc.19th Inter. Joint Conf. on Artificial Intelligence, pp. 266271 (2005) 
8. Valtorta, M., Kim, Y., Vomlel, J.: Soft evidential update for probabilistic multiagent systems.Int. J. Approximate Reasoning 29(1), 71106 (2002) 
9. Vinyals, M., Rodriguez-Aguilar, J., Cerquides, J.: Constructing a unifying theory of dynamic programming DCOP algorithms via the generalized distributive law. J. Autonomous Agents and Multi-Agent Systems 22(3), 439464 (2010) 
10. Xiang, Y.: Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach.Cambridge University Press, Cambridge (2002) 
11. Xiang, Y., Hanshar, F.: Multiagent expedition with graphical models. Inter. J. Uncertainty, Fuzziness and Knowledge-Based Systems 19(6), 939976 (2011) 
12. Xiang, Y., Mohamed, Y., Zhang, W.: Distributed constraint satisfaction with multiply sec- tioned constraint networks. accepted to appear in International J. Information and Decision Sciences (2013) 
13. Xiang, Y., Srinivasan, K.: Construction of privacy preserving hypertree agent organization as distributed maximum spanning tree. In: ZaIane, O., Zilles, S. (eds.) Canadian AI 2013.LNCS (LNAI), vol. 7884, pp. 199210. Springer, Heidelberg (2013) 
-----2
1. Awerbuch, B.: Proc. 19th ACM Symp. Theory of Computing, pp. 230240 (1987) 
2. Brito, I., Meseguer, P.: Cluster tree elimination for distributed constraint optimization with quality guarantees. Fundamenta Informaticae 102(3-4), 263286 (2010) 
3. Faloutsos, M., Molle, M.: Optimal distributed algorithm for minimum spanning trees revis- ited. In: Proc. 14th Annual ACM Symp. Principles of Distributed Computing, pp. 231237 (1995) 
4. Faltings, B., Leaute, T., Petcu, A.: Privacy guarantees through distributed constraint satisfac- tion. In: Proc. IEEE/WIC/ACM Intelligent Agent Technology, pp. 350358 (2008) 
5. Gallager, R., Humblet, P., Spira, P.: A distributed algorithm for minimum-weight spanning trees. ACM Trans. Programming Languages and Systems 5(1), 6677 (1983) 210 Y. Xiang and K. Srinivasan 
6. Garay, J., Kutten, S., Peleg, D.: A sublinear time distributed algorithm for minimum-weight spanning trees. SIAM J. Comput. 27(1), 302316 (1998) 
7. Gmytrasiewicz, P., Durfee, E.: Rational communication in multi-agent environments. Auto.Agents and Multi-Agent Systems 4(3), 233272 (2001) 
8. Jensen, F.: Junction tree and decomposable hypergraphs. Tech. rep., JUDEX, Aalborg, Den- mark (February 1988) 
9. Khan, M., Pandurangan, G.: A fast distributed approximation algorithm for minimum span- ning trees. Distributed Computing 20(6), 391402 (2008) 
10. Koller, D., Milch, B.: Multi-agent influence diagrams for representing and solving games.In: Proc. 17th Inter. Joint Conf. on Artificial Intelligence, pp. 10271034 (2001) 
11. Kutten, S., Peleg, D.: Fast distributed construction of smallk-dominating sets and applica- tions. J. Algorithms 28(1), 4066 (1998) 
12. Maestre, A., Bessiere, C.: Improving asynchronous backtracking for dealing with complex local problems. In: Proc. 16th European Conf. on Artificial Intelligence, pp. 206210 (2004) 
13. Modi, P., Shen, W., Tambe, M., Yokoo, M.: Adopt: asynchronous distributed constraint opti- mization with quality guarantees. Artificial Intelligences 161(1-2), 149180 (2005) 
14. Nobari, S., Cao, T., Karras, P., Bressan, S.: Scalable parallel minimum spanning forest com- putation. In: Proc. 17th ACM SIGPLAN Symp. Principles and Practice of Parallel Program- ming, pp. 205214 (2012) 
15. Paskin, M., Guestrin, C., McFadden, J.: A robust architecture for distributed inference in sensor networks. In: Proc. Information Processing in Sensor Networks, pp. 5562 (2005) 
16. Petcu, A., Faltings, B.: A scalable method for multiagent constraint optimization. In: Proc.19th Inter. Joint Conf. on Artificial Intelligence, pp. 266271 (2005) 
17. Prim, R.: Shortest connection networks and some generalizations. Bell Syst. Tech. J. (36), 13891401 (1957) 
18. Silaghi, M., Abhyankar, A., Zanker, M., Bartak, R.: Desk-mates (stable matching) with pri- vacy of preferences, and a new distributed CSP framework. In: Proc. Inter. Florida Artificial Intelligence Research Society Conf., pp. 8396 (2005) 
19. Valtorta, M., Kim, Y., Vomlel, J.: Soft evidential update for probabilistic multiagent systems.Int. J. Approximate Reasoning 29(1), 71106 (2002) 
20. Vinyals, M., Rodriguez-Aguilar, J., Cerquides, J.: Constructing a unifying theory of dynamic programming DCOP algorithms via the generalized distributive law. J. Autonomous Agents and Multi-Agent Systems 22(3), 439464 (2010) 
21. Xiang, Y.: A probabilistic framework for cooperative multi-agent distributed interpretation and optimization of communication. Artificial Intelligence 87(1-2), 295342 (1996) 
22. Xiang, Y.: Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach.Cambridge University Press, Cambridge (2002) 
23. Xiang, Y., Chen, J., Deshmukht, A.: A decision-theoretic graphical model for collaborative design on supply chains. In: Tawfik, A.Y., Goodwin, S.D. (eds.) Canadian AI 2004. LNCS (LNAI), vol. 3060, pp. 355369. Springer, Heidelberg (2004) 
24. Xiang, Y., Hanshar, F.: Multiagent expedition with graphical models. Inter. J. Uncertainty, Fuzziness and Knowledge-Based Systems 19(6), 939976 (2011) 
25. Xiang, Y., Mohamed, Y., Zhang, W.: Distributed constraint satisfaction with multiply sec- tioned constraint networks. Accepted to appear in International J. Information and Decision Sciences (2013) 
26. Xiang, Y., Srinivasan, K.: Boundary set based existence recognition and construction of hy- pertree agent organization. In: ZaIane, O., Zilles, S. (eds.) Canadian AI 2013. LNCS (LNAI), vol. 7884, pp. 187198. Springer, Heidelberg (2013) 
27. Xiang, Y., Zhang, W.: Multiagent constraint satisfaction with multiply sectioned constraint networks. In: Kobti, Z., Wu, D. (eds.) Canadian AI 2007. LNCS (LNAI), vol. 4509, pp. 228240. Springer, Heidelberg (2007) 
-----2
1. Ammar, A., Elouedi, Z.: A New Possibilistic Clustering Method: The Possibilistic K-Modes. In: Pirrone, R., Sorbello, F. (eds.) AI*IA 2011. LNCS (LNAI), vol. 6934, pp. 413419. Springer, Heidelberg (2011) 
2. Ammar, A., Elouedi, Z., Lingras, P.: K-modes clustering using possibilistic mem- bership. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds.) IPMU 2012, Part III. CCIS, vol. 299, pp. 596605. Springer, Heidelberg (2012) 
3. Ammar, A., Elouedi, Z., Lingras, P.: RPKM: The Rough Possibilistic K-Modes.In: Chen, L., Felfernig, A., Liu, J., Ras, Z.W. (eds.) ISMIS 2012. LNCS, vol. 7661, pp. 8186. Springer, Heidelberg (2012) 
4. Dubois, D., Prade, H.: Possibility theory: An approach to computerized processing of uncertainty. Plenum Press (1988) 
5. Huang, Z.: Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery 2, 283304 (1998) 
6. Huang, Z., Ng, M.K.: A note on k-modes clustering. Journal of Classification 20, 257261 (2003) 
7. Jenhani, I., Ben Amor, N., Elouedi, Z., Benferhat, S., Mellouli, K.: Information affinity: A new similarity measure for possibilistic uncertain information. In: Mel- louli, K. (ed.) ECSQARU 2007. LNCS (LNAI), vol. 4724, pp. 840852. Springer, Heidelberg (2007) 
8. Krishnapuram, R., Keller, J.M.: A possibilistic approach to clustering. IEEE Trans.Fuzzy System 1, 98110 (1993) 
9. MacQueen, J.B.: Some methods for classification and analysis of multivariate ob- servations. In: Proceeding of the Fifth Berkeley Symposium on Math, Stat and Prob., pp. 281296 (1967) 
10. Murphy, M.P, Aha, D.W.: Uci repository databases (1996), http://www.ics.uci.edu/mlearn 
11. Zadeh, L.A.: Fuzzy sets. Information And Control 8, 338353 (1965) 
-----2
1. Armbrust, C., Kiekbusch, L., Ropertz, T., Berns, K.: Verification of behaviour networks using finite-state automata. In: Glimm, B., Kruger, A. (eds.) KI 2012.LNCS, vol. 7526, pp. 112. Springer, Heidelberg (2012) 
2. Armbrust, C., Kiekbusch, L., Ropertz, T., Berns, K.: Tool-assisted verification of behaviour networks. In: ICRA 2013, Karlsruhe, Germany, May 6-10 (2013) 
3. Behrmann, G., David, A., Larsen, K.G.: A tutorial on uppaal. In: Bernardo, M., Corradini, F. (eds.) SFM-RT 2004. LNCS, vol. 3185, pp. 200236. Springer, Hei- delberg (2004) 
4. Clarke, E.M., Grumberg, O., Peled, D.A.: Model Checking. MIT Press (1999) 
5. Eleftherakis, G., Kefalas, P., Sotiriadou, A., Kehris, E.: Modeling biology inspired reactive agents using x-machines. In: Okatan, A. (ed.) International Conference on Computational Intelligence 2004 (ICCI 2004), December 17-19, pp. 9396. Inter- national Computational Intelligence Society, Istanbul (2004) 
6. Faber, J.: Fault tree analysis with Moby/FT. Tech. rep., Department for Comput- ing Science, University of Oldenburg (2005), http://csd.informatik.uni-oldenburg.de/ ~jfaber/dl/ToolPresentationMobyFT.pdf 
7. Juurik, S., Vain, J.: Model checking of emergent behaviour properties of robot swarms. Proceedings of the Estonian Academy of Sciences 60(1), 4854 (2011) 
8. Lowry, M., Havelund, K., Penix, J.: Verification and validation of AI systems that control deep-space spacecraft. In: Ras, Z.W., Skowron, A. (eds.) ISMIS 1997.LNCS, vol. 1325, pp. 3547. Springer, Heidelberg (1997) 
9. Proetzsch, M.: Development Process for Complex Behavior-Based Robot Control Systems. RRLab Dissertations, Verlag Dr. Hut (2010) 
10. Proetzsch, M., Berns, K., Schuele, T., Schneider, K.: Formal verification of safety behaviours of the outdoor robot ravon. In: Zaytoon, J., Ferrier, J.-L., Andrade- Cetto, J., Filipe, J. (eds.) ICINCO 2007, pp. 157164. INSTICC Press (May 2007) 
11. Reichardt, M., Fohst, T., Berns, K.: On software quality-motivated design of a real-time framework for complex robot control systems. In: Proceedings of the 7th International Workshop on Software Quality and Maintainability (SQM), in conjunction with the 17th European Conference on Software Maintenance and Reengineering (CSMR) (March 5, 2013) 
12. Schafer, A.: Combining real-time model-checking and fault tree analysis. In: Araki, K., Gnesi, S., Mandrioli, D. (eds.) FME 2003. LNCS, vol. 2805, pp. 522541.Springer, Heidelberg (2003) 
13. Webster, M., Fisher, M., Cameron, N., Jump, M.: Model checking and the cer- tification of autonomous unmanned aircraft systems. Tech. Rep. ULCS-11-001, University of Liverpool Department of Computer Science (2011) 4 http://concurrency.cs.uni-kl.de/ 
-----2
1. http://archive.ics.uci.edu/ml/ 
2. http://www.norsys.com/ 
3. http://b-course.cs.helsinki.fi/obc/ 
4. https://sites.google.com/site/bayesianoutlier 
5. http://sydney.edu.au/engineering/it/~sakshib/ 
6. Ramaswamy, S., Rastogi, R., Shim, K.: Efficient Algorithms for Mining Outliers from Large Data Sets. In: Proceedings of International Conference on Management of Data, pp. 427438 (2000) 
7. Breunig, M.M., Kriegel, H., Ng, R.T., Sander, J.: LOF: Identifying Density-Based Local Outliers. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 93104 (2000) 
8. Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Tech- niques. MIT Press (2009) 
9. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 9931022 (2003) 
10. Wong, W.K., Moore, A., Cooper, G., Wagner, M.: Bayesian Network Anomaly Pat- tern Detection for Disease Outbreaks. In: Proceedings of International Conference on Machine Learning, pp. 808815 (2003) 
11. Babbar, S., Chawla, S.: On Bayesian Network and Outlier Detection. In: Proceed- ings of International Conference on Management of Data (2010) 
-----2
1. Holte, R.C., Perez, M.B., Zimmer, R.M., MacDonald, A.J.: Hierarchical A*: search- ing abstraction hierarchies effiiently. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence, vol. 1, pp. 530535. AAAI Press (1996) 
2. Botea, A., Muller, M., Schaeffer, J.: Near optimal hierarchical pathfinding. Journal of Game Development 1(1), 728 (2004) 
3. Jansen, M.R., Buro, M.: HPA* enhancements. In: Proceedings of the Third Ar- tificial Intelligence and Interactive Digital Entertainment Conference, Stanford, California, USA, pp. 8487 (2007) 
4. Samet, H.: The design and analysis of spatial data structures, vol. 85, p. 87.Addison-Wesley, Reading (1990) 
5. Duchaineau, M., Wolinsky, M., Sigeti, D.E., Miller, M.C., Aldrich, C., Mineev- Weinstein, M.B.: ROAMing terrain: real-time optimally adapting meshes. In: Pro- ceedings of the IEEE Visualization 1997, pp. 8188 (1997) 
6. Demyen, D.J., Buro, M.: Efficient triangulation-based pathfinding. Masters Ab- stracts International 45(03) (2006) 
7. Dalmau, D.S.C.: Core techniques and algorithms in game programming. New Rid- ers Pub. (2004) 
8. Bulitko, V., Sturtevant, N., Lu, J., Yau, T.: Graph abstraction in real-time heuristic search. JAIR 30, 51100 (2007) 
9. Lindstrom, P., Koller, D., Ribarsky, W., Hodges, L.F., Faust, N., Turner, G.A.: Real-time, continuous level of detail rendering of height fields. In: Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, pp. 109118. ACM (1996) 
10. Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determina- tion of minimum cost paths. IEEE Transactions on Systems Science and Cyber- netics 4(2), 100107 (1968) 
-----2
1. Dickson, P., Adrion, W., Hanson, A.: Automatic Capture of Significant Points in a Computer Based Presentation. In: Eighth IEEE International Symposium on Mul- timedia (ISM 2006), pp. 921926 (2006) 
2. Brooks, C., Amundson, K.: Detecting Significant Events in Lecture Video using Supervised Machine Learning. In: 2009 Conference on Artificial Intelligence in Ed- ucation (2009) 
3. Fleiss, J.L.: Measuring nominal scale agreement among many raters. Psychological Bulletin 76(5), 378382 (1971) 
4. Landis, J.R., Koch, G.G.: The Measurement of Observer Agreement for Categorical Data. Biometrics 33(1), 159174 (1977) 
-----2
1. Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: Data stream mining: a practical approach. Technical report, University of Waikato (May 2011) 
2. de Souza, E.N., Matwin, S.: Extending adaBoost to iteratively vary its base classi- fiers. In: Butz, C., Lingras, P. (eds.) Canadian AI 2011. LNCS (LNAI), vol. 6657, pp. 384389. Springer, Heidelberg (2011) 
3. de Souza, E.N., Matwin, S.: Improvements to adaBoost dynamic. In: Kosseim, L., Inkpen, D. (eds.) Canadian AI 2012. LNCS (LNAI), vol. 7310, pp. 293298. Springer, Heidelberg (2012) 
4. Domingos, P., Hulten, G.: Mining high-speed data streams. In: KDD, pp. 7180 (2000) 
5. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119139 (1997) 
6. Oza, N.C., Russell, S.: Online bagging and boosting. In: Jaakkola, T., Richardson, T. (eds.) 8th International Workshop on Artificial Intelligence and Statistics, Key West, Florida, USA, pp. 105112. M. Kaufmann (2001) 
-----2
1. Agray, N., van der Hoek, W., de Vink, E.: On BAN logics for industrial security proto- cols. In: Dunin-Keplicz, B., Nawarecki, E. (eds.) CEEMAS 2001. LNCS (LNAI), vol. 2296, pp. 2936. Springer, Heidelberg (2002) 
2. Burrows, M., Abadi, M., Needham, R.: A logic of authentication. ACM Transactions on Computer Systems 8(1), 1836 (1990) 
3. Carlucci Aiello, L., Massacci, F.: Verifying Security Protocols as Planning in Logic Program- ming. ACM Transactions on Computational Logic 2(4), 542580 (2001) 
4. Cremers, C.J.F.: The Scyther Tool: Verification, Falsification, and Analysis of Security Pro- tocols. In: Gupta, A., Malik, S. (eds.) CAV 2008. LNCS, vol. 5123, pp. 414418. Springer, Heidelberg (2008) 
5. Dechesne, F., Mousavi, M.R., Orzan, S.: Operational and Epistemic Approaches to Protocol Analysis: Bridging the Gap. In: Dershowitz, N., Voronkov, A. (eds.) LPAR 2007. LNCS (LNAI), vol. 4790, pp. 226241. Springer, Heidelberg (2007) 
6. Dolev, D., Yao, A.C.: On the Security of Public Key Protocols. IEEE Transactions on Infor- mation Theory 2(29), 198208 (1983) 
7. Fagin, R., Halpern, J., Moses, Y., Vardi, M.: Reasoning About Knowledge. MIT Press (1995) 
8. Gong, L., Needham, R., Yahalom, R.: Reasoning About Belief in Cryptographic Protocols.In: Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy, pp. 234248 (1990) 
9. Guttman, J., Thayer, J.: Authentication tests. In: Proceedings of the 2000 IEEE Symposium on Security and Privacy (2000) 
10. Halpern, J., Pucella, R.: On the relationship between strand spaces and multi-agent systems.ACM Transactions on Information and System Security (TISSEC) 6(1) (2003) 
11. Hunter, A., Delgrande, J.P.: Belief Change and Cryptographic Protocol Verification. In: Pro- ceedings of the National Conference on Artificial Intelligence, AAAI 2007 (2007) 
12. Hunter, A.: Dissecting the Meaning of an Encrypted Message: An Approach to Discover- ing the Goals of an Adversary. In: Ortiz-Arroyo, D., Larsen, H.L., Zeng, D.D., Hicks, D., Wagner, G. (eds.) EuroISI 2008. LNCS, vol. 5376, pp. 6172. Springer, Heidelberg (2008) 
13. Kramer, S.: Reducing Provability to Knowledge in Multi-Agent Systems. In: Intuitionistic Modal Logics and Applications Workshop, IMLA 2008 (2008) 
14. Syverson, P.: Knowledge, Belief, and Semantics in the Analysis of Cryptographic Protocols.Journal of Compuer Security 1, 317334 (1992) 
15. Syverson, P., Cervesato, I.: The logic of authentication protocols. In: Focardi, R., Gorrieri, R. (eds.) FOSAD 2000. LNCS, vol. 2171, pp. 63137. Springer, Heidelberg (2001) 
-----2
1. Hafting, T., Fyhn, M., Molden, S., Moser, M., Moser, E.: Microstructure of a spatial map in the entorhinal cortex. Nature 436(7052), 801806 (2005) 
2. Welday, A.C., Shlifer, I.G., Bloom, M.L., Zhang, K., Blair, H.T.: Cosine Directional Tuning of Theta Cell Burst Frequencies: Evidence for Spatial Coding by Oscillatory Interference. Journal of Neuroscience 31(45), 1615716176 (2011) 
3. Zilli, E.A., Hasselmo, M.E.: Coupled Noisy Spiking Neurons as Velocity-Controlled Oscillators in a Model of Grid Cell Spatial Firing. Journal of Neuroscience 30(41), 1385013860 (2010) 
4. Orchard, J., Yang, H., Ji, X.: Navigation by path integration and the fourier trans- form: A spiking-neuron model. In: ZaIane, O., Zilles, S. (eds.) Canadian AI 2013.LNCS (LNAI), vol. 7884, pp. 138149. Springer, Heidelberg (2013) 
5. Blair, H., Welday, A.C., Zhang, K.: Scale-Invariant Memory Representations Emerge from Moire Interference between Grid Fields That Produce Theta Oscilla- tions: A Computational Model. Journal of Neuroscience 27(12), 32113229 (2007) 
6. Burgess, N., Barry, C., OKeefe, J.: An oscillatory interference model of grid cell firing. Hippocampus 17(9), 801812 (2007) 
7. Hasselmo, M.E., Brandon, M.P.: Linking Cellular Mechanisms to Behavior: En- torhinal Persistent Spiking and Membrane Potential Oscillations May Underlie Path Integration, Grid Cell Firing, and Episodic Memory. Neural Plasticity (2008) 
8. Etienne, A.S., Maurer, R., Seguinot, V.: Path Integration in Mammals and its Interaction with Visual Landmarks. Journal of Experimental Biology 199, 201209 (1996) 
9. Williams, J.M., Givens, B.: Stimulation-induced reset of hippocampal theta in the freely performing rat. Hippocampus 13(1), 109116 (2003) 
10. Conklin, J., Eliasmith, C.: A Controlled Attractor Network Model of Path Inte- gration in the Rat. Journal of Computational Neuroscience 18, 183203 (2005) 
11. Eliasmith, C., Anderson, C.H.: Neural engineering: Computation, representation, and dynamics in neurobiological systems. MIT Press, Cambridge (2003) 
-----2
1. Lin, F., Reiter, R.: Forget it! Working Notes of AAAI Fall Symposium on Relevance, pp. 154159 (1994) 
2. Lang, J., Liberatore, P., Marquis, P.: Propositional independence. Journal of Arti- ficial Intelligence Research 18, 391443 (2003) 
3. Lang, J., Marquis, P.: Resolving inconsistencies by variable forgetting. In: Inter- national Conference on Principles of Knowledge Representation and Reasoning, pp. 239250. Morgan Kaufmann Publishers (2002) 
4. Lang, J., Marquis, P.: Reasoning under inconsistency: A forgetting-based approach.Artificial Intelligence 174(12-13), 799823 (2010) 
5. Bertossi, L., Hunter, A., Schaub, T. (eds.): Inconsistency Tolerance. LNCS, vol. 3300. Springer, Heidelberg (2005) 
6. Xu, D., Lin, Z.: A prime implicates-based formulae forgetting. In: 2011 IEEE In- ternational Conference on Computer Science and Automation Engineering (CSAE), vol. 3, pp. 128132. IEEE (2011) 
7. Wang, Z., Wang, K., Topor, R., Pan, J.Z.: Forgetting concepts in DL-lite. In: Bech- hofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 245257. Springer, Heidelberg (2008) 
8. Eiter, T., Wang, K.: Semantic forgetting in answer set programming. Artificial In- telligence 172(14), 16441672 (2008) 
9. Zhang, Y., Zhou, Y.: Knowledge forgetting: Properties and applications. Artificial Intelligence 173(16), 15251537 (2009) 
-----2
1. Wolpert, D., Macready, W.: No free lunch theorems for optimization. IEEE Trans. Evol.  Comput. 1, 6782 (19 97)  
2. Gama, J.: Combining classification algorithms. PhD Thesis, University of Porto (1999)  
3. Schapire, R.E.: The strength of weak learnability. Machine Learning 5, 197227 (1990)  
4. Philip, K.C., Salvatore, J.S.: Meta-learning for multistrategy and parallel learning. In: Proc.  Second Intl. Work. Multistraegy Learning, pp. 150165 (1993)  
5. de Souza, .N., Matwin, S.: Extending AdaBoost to Iteratively Vary Its Base Classifiers.  In: Butz, C., Lingras, P. (eds.) Canadian AI 2011. LNCS, vol. 6657, pp. 384389.  Springer, Heidelberg (2011)  
6. Kohavi, R., Wolpert, D.H.: Bias plus variance decomposition for zero-one loss functions.  In: 13th International Conference on Machine Learning, pp. 275283 (1996)  
7. Dietterich, T.G.: Bias-variance analysis of ensemble learning. In: 7th Course of the  International School on Neural Networks, Ensemble Methods for Learning Machines  (2002)  
8. Holte, R.C.: Very simple classification rules perform well on most commonly used  datasets. Machine Learning 11, 6391 (1993)  
9. Domingos, P., Pazzani, M.: On the optimality of the simple Bayesian classifier under zero- one loss. Machine Learning 29, 103130 (1997)  
10. Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms:  Bagging, boosting, and variants. Machine Learning 36, 105139 (1999)  
11. Domingos, P.: A unified bias-variance decomposition. In: Proceedings of 17th  International Conference on Machine Learning. Morgan Kaufmann, Stanford (2000)  
12. Webb, I., Zheng, Z.: Multistrategy ensemble learning: Reducing error by combining  ensemble learning technique. IEEE Tran. on Knowledge and Data Engineering 16,   980991 (2004)  
13. Philip, K.C., Salvatore, J.S.: Scaling learning by metalearning over disjoint and partially  replicated data. In: Proc. Ninth Florida AI Research Symposium, pp. 151155 (1996)  
14. Frank, A., Asuncion, A.: UCI machine learning repository (2010)  
15. Aha, D.W.: Lazy learning. Kluwer Academic Publishers (1997)  
16. Quinlan, J.R.: C4.5: Programs for machine learning, vol. 1. Morgan Kaufmann (1993)  
17. Kotsiantis, S.B., Zaharakis, I.D., Pintelas, P.E.: Machine learning: A review of  classification and combining techniques. Artificial Intelligence Review 26, 159190  (2006)  
18. Japkowicz, N., Mohak, S.: Evaluating Learning Algorithms: A Classification Perspective.  Cambridge University Press (2011)  
-----2
1. Brans, J.P., Vincke, P., Mareschal, B.: How to select and how to rank projects: The promethee method. European Journal of Operational Research 24(2), 228238 (1986) 
2. Chen, C.: Top 10 unsolved information visualization problems. IEEE Computer Graphics and Applications 25(4), 1216 (2005) 
3. Elmqvist, N., Yi, J.S.: Patterns for visualization evaluation (2012) 
4. Goldberg, J., Helfman, J.: Eye tracking for visualization evaluation: Reading values on linear versus radial graphs. Information Visualization 10(3), 182195 (2011) 
5. Hartigan, J.A., Wong, M.A.: Algorithm as 136: A k-means clustering algorithm.Applied Statistics, 100108 (1979) 
6. Maystre, L.Y., Pictet, J., Simos, J.: Methodes multicrite`res ELECTRE: Descrip- tion, conseils pratiques et cas dapplication a` la gestion environnementale, vol. 8.PPUR (1994) 
7. McNamara, A., Mania, K., Gutierrez, D.: Perception in graphics, visualization, virtual environments and animation. In: SIGGRAPH Asia 2011 Courses, p. 17.ACM (2011) 
8. Mena, S.B.: Une solution informatisee a` lanalyse de sensibilite delectre iii. Biotech- nol. Agron. Soc. Environ. 5(1), 3135 (2001) 
9. Mouine, M., Lapalme, G.: Using clustering to personalize visualization. In: 2012 16th International Conference on Information Visualisation (IV), pp. 258263.IEEE (2012) 
10. Plaisant, C.: The challenge of information visualization evaluation. In: Proceedings of the Working Conference on Advanced Visual Interfaces, pp. 109116. ACM (2004) 
11. Robbins, N.B.: Creating more effective graphs. Wiley-Interscience (2012) 
12. Roy, B.: Classement et choix en presence de points de vue multiples (la methode electre). RIRO 2(8), 5775 (1968) 
13. Roy, B.: Electre iii: Un algorithme de classement fonde sur une representation floue des preferences en presence de crite`res multiples. Cahiers du CERO 20(1), 324 (1978) 
14. Ward, M., Grinstein, G., Keim, D.: Interactive data visualization: Foundations, techniques, and applications. AK Peters, Ltd. (2010) 
15. Ware, C.: Information visualization: Perception for design. Morgan Kaufmann (2012) 
16. Weaver, C.: Look before you link: Eye tracking in multiple coordinated view visu- alization. In: BELIV 2010: BEyond Time and Errors: Novel Evaluation Methods for Information Visualization, p. 2 (2010) 
-----2
1. Blei, D., Ng, A., Jordan, M.: Latent Dirichlet allocation. Journal of Machine Learning Re- search 3, 9931022 (2003)  
2. Teh, Y.-W., Newman, D., Welling, M.: A collapsed variational Bayesian inference algo- rithm for latent Dirichlet allocation. In: Procs. NIPS (2006)  
3. Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proceedings of the National Acad- emy of Sciences 101, 52285235 (2004)  
4. Minka, T., Lafferty, J.: Expectation propagation for the generative aspect model. In: Pro- ceedings of UAI (2002)  
5. Wang, X., McCallum, A.: Topics over time: A non-markov continuous-time model of top- ical trends. In: Proceedings of KDD (2006)  
6. Blei, D.M., McAulie, J.: Supervised topic models. In: Procs. of NIPS (2007)  
7. Li, W., McCallum, A.: Pachinko allocation: Dag-structured mixture models of topic corre- lations. In: ICML (2006)  
8. Hoffman, M., Blei, D., Bach, F.: Online learning for latent Dirichlet allocation. In: Pro- ceedings of NIPS (2010)  
9. Wahabzada, M., Kersting, K.: Larger residuals, less work: Active document scheduling for  latent dirichlet allocation. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M.  (eds.) ECML PKDD 2011, Part III. LNCS, vol. 6913, pp. 475490. Springer, Heidelberg  (2011)  
10. Heinrich, G.: Parameter estimation for text analysis, Technical Report (For further infor- mation please refer to JGibbLDA at: http://jgibblda.sourceforge.net/)  
11. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Synthetic Minority Over- sampling Technique. Journal of Artificial Intelligence Research 16, 321357 (2002)  
12. Wang, A., Hoang, C.D.V., Kan, M.-Y.: Perspectives on crowdsourcing annotations for  natural language processing. Language Resources and Evaluation, 123 (2012)  
13. Ferschke, O., Daxenberger, J., Gurevych, I.: A Survey of NLP Methods and Resources for  Analyzing the Collaborative Writing Process in Wikipedia (2012)  
14. Fleischmann, K.R., Templeton, C., Boyd-Graber, J., Cheng, A.-S., Oard, D.W., Ishita, E.,  Koepfler, J.A., Wallace, W.A.: Explaining Sentiment Polarity: Automatic Detection of  Human Values in Texts (2012) (to appear)      
-----2
1. AbdelRahman, S., Hassan, B., Bahgat, R.: A new email retrieval ranking approach.CoRR, abs/1011.0502 (2010) 
2. Balasubramanyan, R., Carvalho, V.R., Cohen, W.: Cutonce- recipient recommen- dation and leak detection in action (2008) 
3. Bennett, P.N., Carbonell, J.G.: Combining probability-based rankers for action- item detection. In: HLT-NAACL 2007, pp. 324331 (2007) 
4. Corrada-Emmanuel, A. (n.d.).: Enron Email Dataset Research (2004) 
5. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 39(1), 138 (1977) 
6. Diesner, J., Carley, K.M.: Exploration of Communication Networks from the Enron Email Corpus. In: Proceedings of Workshop on Link Analysis, Counterterrorism and Security, SIAM International Conference on Data Mining 2005, pp. 314 (2005) 
7. Klimt, B., Yang, Y.: The Enron Corpus: A New Dataset for Email Classification Research. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 217226. Springer, Heidelberg (2004) 
8. McCallum, A., Wang, X., Corrada-Emmanuel, A.: Topic and role discovery in social networks with experiments on enron and academic email. Journal of Artificial Intelligence Research 30(1), 249272 (2007) 
9. Mobasher, B.: Web usage mining and personalization. CRC Press (2005) 
10. Neustaedter, C., Bernheim Brush, A.J., Smith, M.A., Fisher, D.: The social net- work and relationship finder: Social sorting for email triage. In: CEAS 2005 - Second Conference on Email and Anti-Spam, Stanford University, California, USA, July 21-22 (2005) 
11. Newman, M.E.J.: Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103(23), 85778582 (2006) 
12. On, B.-W., Lim, E.-P., Jiang, J., Purandare, A., Teow, L.-N.: Mining interaction behaviors for email reply order prediction. In: 2010 International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 306310 (Au- gust 2010) 
13. Shetty, J., Adibi, J.: The Enron email dataset database schema and brief statistical report. Information Sciences Institute Technical Report, University of Southern California (2004) 
14. Wang, M.-F., Jheng, S.-L., Tsai, M.-F., Tang, C.-H.: Enterprise email classifica- tion based on social network features. In: Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011, pp. 532536. IEEE Computer Society, Washington, DC (2011) 
15. Yoo, S., Yang, Y., Lin, F., Moon, I.-C.: Mining social networks for personalized email prioritization. In: Proceedings of the 15th ACM SIGKDD International Con- ference on Knowledge Discovery and Data Mining, KDD 2009, pp. 967976. ACM, New York (2009) 
-----2
1. Stolcke, A.: Srilm-An Extensible Language Modeling Toolkit. In: Proc. of the Internation- al Conference on Spoken Language Processing (2002)  
2. Diab, M., Hacioglu, K., Jurafsky, D.: Automatic Tagging of Arabic Text: From Raw Text  to Base Phrase Chunks. In: Proc. of the North American Chapter of the Association for  Computational Linguistics (NAACL), Boston, MA (2004)  
3. Koehn, P., Shen, W., Federico, M., Bertoldi, N., Callison-Burch, C., Cowan, B., Dyer, C.,  Hoang, H., Bojar, O., Zens, R., Constantin, A., Herbst, E., Moran, C., Birch, A.: Moses:  Open source toolkit for statistical machine translation. In: Proceedings of the ACL 2007  Interactive Presentation Sessions, Prague (2007)  
4. Carpuat, M., Marton, Y., Habash, N.: Improving Arabic-to-English Statistical Machine  Translation by Reordering Post-verbal Subjects for Alignment. Machine Translation, Spe- cial Issue on Machine Translation for Arabic 26(1-2), 105120 (2012)  
5. Habash, N.: Introduction to Arabic Natural Language Processing. Morgan & Claypool  (2010)  
6. Habash, N., Sadat, F.: Arabic Preprocessing Schemes for Statistical Machine Translation.  In: Proceedings of NAACL 2006, New York, USA, June 5-7 (2006)  
7. Lee, Y.: Morphological Analysis for Statistical Machine Translation. In: Proc. of NAACL,  Boston, MA (2004)  
8. Papineni, K., Roukos, S., Ward, T., Zhu, W.: Bleu: A Method for Automatic Evaluation of  Machine Translation. Technical Report RC22176(W0109-022), IBM Research Division,  Yorktown Heights, NY (2001)  
9. Daelemans, W., Zavrel, J., Berck, P., Gillis, S.: MBT: A memory part speech tagger gene- rator. In: Proceedings of the Fourth Workshop on Very Large Corpora, ACL 1996, Copen- hagen, Denmark, pp. 1427 (August 4, 1996)  
10. Mohamed, E., Kbler, S.: Arabic part of speech tagging. In: Proceedings of LREC, Valet- ta, Malta (2010)  
11. Hasan, S., Isbihani, A.E., Ney, H.: Creating a Large-Scale Arabic to French Statistical Ma- chine Translation System. In: International Conference on Language Resources and Evalu- ation (LREC), Genoa, Italy, pp. 855858 (May 2006)  
12. El Isbihani, A., Khadivi, S., Bender, O., Ney, H.: Morpho-syntactic Arabic Preprocessing  for Arabic to English Statistical Machine Translation. In: Human Language Technology  Conf./North American Chapter of the Assoc. for Computational Linguistics Annual Meet- ing (HLT-NAACL), Workshop on Statistical Machine Translation, New York City,   pp. 1522 (June 2006)  
13. Och, F.J., Ney, H.: A Systematic Comparison of Various Statistical Alignment Models.  Computational Linguistics 29(1), 1951 (2003)  
-----2
1. Endsley, M.: Toward a Theory of Situation Awareness in Dynamic Systems. Human Factors: The Journal of the Human Factors and Ergonomics Society 37(1) (1995) 
2. Lambert, D.A.: STDF Model based Maritime Situation Assessments. In: 2007 10th International Conference on Information Fusion. IEEE (2007) 
3. Vaseqi, Z.: A Prototype Implementation for Situation Analysis using ASP and Core- ASM. Masters thesis, Simon Fraser University (2012) 
4. Gelfond, M., Lifschitz, V.: The Stable Model Semantics for Logic Programming.In: Proceedings of the 5th International Conference on Logic programming, vol. 161 (1988) 
5. Gebser, M., Grote, T., Kaminski, R., Schaub, T.: Reactive Answer Set Program- ming. In: Delgrande, J.P., Faber, W. (eds.) LPNMR 2011. LNCS, vol. 6645, pp.5466. Springer, Heidelberg (2011) 
6. Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T., Thiele, S.: Engineering an incremental asp solver. In: Logic Programming (2008) 
7. Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T., Thiele, S.: A Users Guide to gringo, clasp, clingo, and iclingo. University of Potsdam. Tech. Rep.(2008) 
8. Farahbod, R.: CoreASM: An Extensible Modeling Framework & Tool Environment for High-level Design and Analysis of Distributed Systems. PhD thesis, Simon Fraser University (2009) 
-----2
1. Alanazi, E., Mouhoub, M.: Arc consistency for cp-nets under constraints.In: FLAIRS Conference (2012) 
2. Alanazi, E., Mouhoub, M.: A framework to manage conditional constraints and qualitative preferences. In: FLAIRS Conference (to appear, 2013) 
3. Boutilier, C., Brafman, R.I., Domshlak, C., Hoos, H.H., Poole, D.: Cp-nets: A tool for representing and reasoning with conditional ceteris paribus preference state- ments. J. Artif. Intell. Res. (JAIR) 21, 135191 (2004) Preference Constrained Optimization under Change 327 
4. Boutilier, C., Brafman, R.I., Hoos, H.H., Poole, D.: Preference-based constrained optimization with cp-nets. Computational Intelligence 20, 137157 (2001) 
5. Darwiche, P.A.: Modeling and Reasoning with Bayesian Networks, 1st edn. Cam- bridge University Press, New York (2009) 
6. Domshlak, C., Rossi, F., Venable, K.B., Walsh, T.: Reasoning about soft con- straints and conditional preferences: complexity results and approximation tech- niques. CoRR abs/0905.3766 (2009) 
7. Doyle, J., Thomason, R.H.: Background to qualitative decision theory.AI Magazine 20 (1999) 
8. Goldsmith, J., Junker, U.: Preference handling for artificial intelligence. AI Maga- zine 29(4), 912 (2008) 
9. Kumar, V.: Algorithms for constraint satisfaction problems: A survey. AI Maga- zine 13(1), 3244 (1992) 
10. Mackworth, A.K.: Consistency in networks of relations. Artificial Intelligence 8(1), 99118 (1977) 
11. Prestwich, S., Rossi, F., Venable, K.B., Walsh, T.: Constrained cpnets. In: Pro- ceedings of CSCLP 2004 (2004) 
12. Rossi, F., Venable, K.B., Walsh, T.: Preferences in constraint satisfaction and op- timization. AI Magazine 29(4), 5868 (2008) 
13. Wilson, N.: Consistency and constrained optimisation for conditional preferences.In: ECAI, pp. 888894 (2004) 
-----2
1. Collins, F.S., Morgan, M., Patrinos, A.: The human genome project: Lessons from large- scale biology. Science 300, 286290 (2003)  
2. Wright, A., Hastie, N.: Genes and Common Diseases. Cambridge University Press, New  York (2007)  
3. Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning: Data mining,  inference, and prediction, 2nd edn. Springer, New York (2009)  
4. Hajiloo, M., Damavandi, B., Hooshsadat, M., Sangi, F., Cass, C.E., Mackey, J., Greiner, R.,  Damaraju, S.: Using genome wide single nucleotide polymorphism data to learn a model for  breast cancer prediction. BMC Bioinformatics (in press)  
5. Hajiloo, M., Sapkota, Y., Mackey, J.R., Robson, P., Greiner, R., Damaraju, S.:  ETHNOPRED: A novel machine learning method for accurate continental and sub- continental ancestry identification and population stratification correction. BMC Bioinfor- matics 14(1), 61 (2013)  
6. Valiant, L.G.: A theory of learnable. Communications of the ACM 27, 11341142 (1984)  
7. Vapnik, V., Chervonenkis, A.: On the uniform convergence of relative frequencies of events  to their probabilities. Theory of Probability and its Applications 16(2), 264280 (1971)  
8. Bertucci, F., Birnbaum, D.: Reasons for breast cancer heterogeneity. Journal of Biology  7(2), 6 (2008)    
-----2
1. AlKhateeb, J., Ren, J., Ipson, S.S., Jiang, J.: Knowledge-based Baseline Detection and   Optimal Thresholding for Words Segmentation in Efficient Pre-processing of Handwritten  Arabic Text. In: 5th International Conference on Information Technology: New Generations  (ITNG), Las Vegas, USA, pp. 11581159 (2008)  
2. Srihari, S., Srinivasan, H., Babu, P., Bhole, C.: Spotting Words in Handwritten Arabic   Documents. In: Document Recognition and Retrieval XIII (SPIE), San Jose, USA,   pp. 606702_1606702_12 (2006)  
3. Elgammal, A., Ismail, M.: A Graph-based Segmentation and Feature Extraction Framework  for Arabic Text Recognition. In: 6th International Conference on Document Analysis and  Recognition (ICDAR), Seattle, USA, pp. 622626 (2001)  
4. Kchaou, M., Kanoun, S.: Segmentation and Word Spotting Methods for Printed and  Handwritten Arabic Texts: A Comparative Study. In: 13th International Conference on  Frontiers in Handwriting Recognition (ICFHR), Bari, Italy, pp. 274279 (2012)  
5. Pechwitz, M., Abed, H., Mrgner, V.: Handwritten Arabic Word Recognition Using the  IFN/ENIT database. In: Guide to OCR for Arabic Scripts, pp. 169213 (2012)  
6. Pechwitz, M., Maddouri, S., Mrgner, V., Ellouze, N., Amiri, H.: IFN/ENIT- database   of Handwritten Arabic Words. In: International Symposium on Frontiers in Writing and  Document (CIFED), Hammamet, Tunisia, pp. 129136 (2002)    
-----2
1. Chen, L., Khalil, I.: Activity recognition: Approaches, practices and trends. In: Ac- tivity Recognition in Pervasive Intelligent Environments, pp. 132. Atlantis Press (2011) 
2. Fiore, L., Fehr, D., Bodor, R., Drenner, A., Somasundaram, G., Papanikolopoulos, N.: Multi-camera human activity monitoring. Journal of Intelligent and Robotic Systems 52(1), 543 (2008) 
3. Schulze, B., Floeck, M., Litz, L.: Concept and design of a video monitoring system for activity recognition and fall detection. In: Mokhtari, M., Khalil, I., Bauchet, J., Zhang, D., Nugent, C. (eds.) ICOST 2009. LNCS, vol. 5597, pp. 182189. Springer, Heidelberg (2009) 
4. Yu, X.: Approaches and principles of fall detection for elderly and patient. In: IEEE 10th International Conference on ehealth Networking Applications and Services, HealthCom 2008, pp. 4247 (2008) 
5. Zhang, D., Gatica-Perez, D., Bengio, S., McCowan, I.: Semi-supervised adapted HMMs for unusual event detection. In: CVPR (1), pp. 611618 (2005) 
6. Yin, J., Yang, Q., Pan, J.J.: Sensor-based abnormal human-activity detection.IEEE Trans. Knowl. Data Eng. 20(8), 10821090 (2008) 
7. Lane, N., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., Campbell, A.: A survey of mobile phone sensing. IEEE Communications Magazine 48(9), 140150 (2010) 
8. Yang, J., Xu, Y.: Hidden markov model for gesture recognition. Technical Report CMU-RI-TR-94-10, Robotics Institute, Pittsburgh, PA (May 1994) 
9. Bowyer, K.W., Chawla, N.V., Hall, L.O., Kegelmeyer, W.P.: SMOTE: Synthetic minority over-sampling technique. CoRR abs/1106.1813 (2011) 
10. Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: Proba- bilistic models for segmenting and labeling sequence data. In: Brodley, C.E., Dany- luk, A.P. (eds.) ICML, pp. 282289. Morgan Kaufmann (2001) 
11. Khan, S.S., Karg, M.E., Hoey, J., Kulic, D.: Towards the detection of unusual temporal events during activities using hmms. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, UbiComp 2012, pp. 10751084. ACM, New York (2012) 
-----2
1. Iqbal, S.T., Zheng, X.S., Bailey, B.P.: Task-evoked pupillary response to mental workload in human-computer interaction. Ext. Abstracts CHI 2004, pp. 14771480.ACM Press (2004) 
2. Juris, M., Velden, M.: The Pupillary Response to Mental Overload. Physiological Psychology 5(4), 421424 (1977) 
3. Nkambou, R., Bourdeau, J., Mizoguchi, R.: Advances in Intelligent Tutoring Sys- tems. Springer, Heidelberg (2010) 978-3-642-14362-5 
4. Wenger, E.: Artificial Intelligence and Tutoring Systems. Computational ap- proaches to the communication of knowledge (1987) 
5. Pivec, M., Trummer, C., Pripfl, J.: Eye-Tracking Adaptable e-Learning and Con- tent Authoring Support. Informatica 30, 8386 (2006) 
6. Calvi, C., Porta, M., Sacchi, D.: e5Learning, an E-Learning Environment Based on Eye Tracking. In: Proceedings of the 8th IEEE International Conference on Advanced Learning Technologies (ICALT 2008), Santander, Spain, July 1-5, pp. 376380 (2008) 
7. Wang, H., Chignell, M., Ishizuka, M.: Empathic Tutoring Software Agents Using Real-time Eye Tracking. In: Proc. of the Eye Tracking Research and Applications Symposium, ETRA 2006, pp. 7378 (2006) 
8. DMello, S., Olney, A., Williams, C., Hays, P.: Gaze tutor: A gaze-reactive intel- ligent tutoring system. International Journal of Human-Computer Studies 70(5), 377398 (2012) 
9. Lowenstein, O.: Experimentelle Beitrage zur Lehre von den Katatonischen Pupil- lenveranderungen. Monatschrift far Psychiatrie und Neurologie 47, 194215 (1920) 
10. Hass, E.H., Polt, J.M.: Pupil size in relation to mental activity during simple problem-solving. Science 143, 11901192 (1964) 
11. Hoeks, B., Levelt, W.J.M.: Pupillary dilation as a measure of attention: A quan- titative system analysis. Behavior Research Methods, Instruments and Comput- ers 25(1), 1626 (1993) 
12. Beatty, J.: Task-Evoked Pupillary Responses, Processing Load, and the Structure of Processing Resources. Psychological Bulletin 91(2), 276292 (1982) 
13. Marshall, S.: The Index of Cognitive Activity: Measuring Cognitive Workload. In: Proceedings of IEEE Conference on Human Factors and Power Plants, pp. 79 (2002) 
14. Bartels, M., Marshall, S.: Measuring cognitive workload across different eye track- ing hardware platforms. In: Proc. of ETRA 2012, pp. 161164 (2012) 
-----2
1. The sparse representation toolbox in matlab, http://cs.uwindsor.ca/  li11112c/sr 
2. Bruckstein, A.M., Donoho, D.L., Elad, M.: From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Review 51(1), 3481 (2009) 
3. Elad, M.: Sparse and Redundant Representations. Springer, New York (2010) 
4. Gao, S., Tsang, I.W.-H., Chia, L.-T.: Kernel sparse representation for image classification and face recognition. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV.LNCS, vol. 6314, pp. 114. Springer, Heidelberg (2010) 
5. Jenatton, R., Mairal, J., Obozinski, G., Bach, F.: Proximal methods for hierarchical sparse coding. JMLR 12(2011), 22972334 (2011) 
6. Kim, S.J., Koh, K., Lustig, M., Boyd, S., Gorinevsky, D.: An interior-point method for large- scale l1-regularized least squares. J-STSP 1(4), 606617 (2007) 
7. Li, Y., Ngom, A.: Non-negative matrix and tensor factorization based classification of clinical microarray gene expression data. In: BIBM, pp. 438443. IEEE Press, Piscataway (2010) 
8. Li, Y., Ngom, A.: Fast kernel sparse representation approaches for classification. In: ICDM, pp. 966971. IEEE Press, Piscataway (2012) 
9. Li, Y., Ngom, A.: Fast sparse representation approaches for the classification of high- dimensional biological data. In: BIBM, pp. 306311. IEEE Press, Piscataway (2012) 
10. Li, Y., Ngom, A.: Supervised dictionary learning via non-negative matrix factorization for classification. In: ICMLA, pp. 439443. IEEE Press, Piscataway (2012) 
11. Li, Y., Ngom, A.: Classification approach based on non-negative least squares. Neurocom- puting (in press, 2013) 
12. Li, Y., Ngom, A.: The non-negative matrix factorization toolbox for biological data mining.BMC Source Code for Biology and Medicine (2013), http://cs.uwindsor.ca/  li11112c/nmf (under revision) 
13. Li, Y., Ngom, A.: Sparse representation approaches for the classification of high-dimensional biological data. BMC Systems Biology (in press, 2013) 
14. Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statis- tical Society. Series B (Methodological) 58(1), 267288 (1996) 
15. Wright, J., Yang, A., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. TPAMI 31(2), 210227 (2009) 
16. Yin, J., Liu, X., Jin, Z., Yang, W.: Kernel sparse representation based classification. Neu- rocmputing 77, 120128 (2012) 
-----2
1. Caylor, J.S., Stitch, T.G., Fox, L.C., Ford, J.P.: Methodologies for determining reading requirements of military occupational specialities. Technical Report 73-5, Human Resources Research Organization, Alexandria, VA (1973) 
2. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16, 321357 (2002) 
3. Duffy, T.M., Kabance, P.: Testing a readable writing approach to text revision.Journal of Educational Psychology 74, 733748 (1982) 
4. Faiz, S.I.: Discovering higher order relations from biomedical text. Masters thesis, Department of Computer Science, The University of Western Ontario, Canada (2012) 
5. Flesch, R.: A new readability yardstick. Journal of Applied Psychology 32, 221233 (1948) 
6. Fundel, K., Kuffner, R., Zimmer, R.: Relex - relation extraction using dependency parse trees. BMC Bioinformatics 23(3), 365371 (2007) 
7. Gunning, R.: Fog index after twenty years. Journal of Business Communica- tion 6(3), 313 (1969) 
8. Kincaid, J.P., Fishburne, R.P., Rogers, R.L., Chissom, B.S.: Derivation of new readability formulas (automated readability index, fog count, and flesch reading ease formula) for navy enlisted personnel. Research Branch Report 8-75, Chief of Naval Technical Writing: Naval Air Station Memphis (1975) 
9. McLaughlin, G.H.: Smog grading  a new redability formula. Journal of Read- ing 12(8), 63946 (1969) 
10. Medelyan, O.: Human-competitive automatic topic indexing. PhD thesis, Univer- sity of Waikato, New Zealand (2009) 
11. Medelyan, O., Witten, I.: Domain-independent automatic keyphrase indexing with small training sets. Journal of the American Society for Information Science and Technology (JASIST) 59(7), 10261040 (2008) 
12. Perez-Iratxeta, C., Bork, P., Andrade, M.: Literature and genome data mining for prioritizing disease-associated genes. In: Eisenhaber, F. (ed.) Discovering Biomolec- ular Mechanisms with Computational Biology. Molecular Biology Intelligence Unit, pp. 7481. Springer (2006) 
13. Shams, R., Mercer, R.E.: Extracting connected concepts from biomedical texts using fog index. Elsevier Procedia - Social and Behavioral Sciences 27, 7076 (2011) 
14. Shams, R., Mercer, R.E.: Evaluating core measures of text denoising for biomed- ical relation mining. In: 3rd International Workshop on Global Collaboration of Information Schools (WIS 2012), Taipei, Taiwan (2012) 
15. Shams, R., Mercer, R.E.: Improving supervised keyphrase indexer classification of keyphrases with text denoising. In: Chen, H.-H., Chowdhury, G. (eds.) ICADL 2012. LNCS, vol. 7634, pp. 7786. Springer, Heidelberg (2012) 
16. Shams, R., Mercer, R.E.: Investigating keyphrase indexing with text denoising. In: 12th ACM/IEEE Joint Conference on Digital Libraries (JCDL 2012), pp. 263266.ACM (2012) 
17. Witten, I., Paynter, G., Frank, E., Gutwin, C., Nevill-Manning, C.: KEA: Practical automatic keyphrase extraction. In: Proceedings of the 4th ACM Conference on Digital Libraries, Berkeley, CA, USA, pp. 254255 (1999) 
-----2
1. Sultana, M., Gavrilova, M.: A Content Based Feature Combination Method for Face   Recognition. In: 8th International Conf. on Computer Recognition Systems (CORES),   Poland, May 27-29 (in press, 2013)  
2. Sultana, M., Uddin, M.S.: Trademark Recognition using a Weighted Combination   of Different Image Features. Int. J. of Computer Theory and Engineering (IJCTE) 4(6),  10351038 (2012)  A Novel Content Based Methodology for a Large Scale Multimodal Biometric System 369  
3. Sultana, M., Mamun, N.M., Uddin, M.S., Ali, M.: A GPU Based Efficient Trademark Re- trieval Technique using a Weighted Combination of Multiple Image Features. In: IEEE  Conference on Communication, Science & Information Engg. (CCSIE), London, UK,   pp. 8388 (2011)  
4. Chora?, R.S.: Image Feature Extraction Techniques and Their Applications for CBIR and  Biometric Systems. Int. J. of Biology and Biomedical Engineering 1(1), 616 (2007)  
5. Passport Canada,  http://www.ppt.gc.ca/support/faq.aspx?lang=eng&id=q810   (last accessed on March 05, 2013)  
6. CICS News: Nationals of 29 Countries to Require Biometrics to Enter Canada (December  10, 2012), http://www.cicsnews.com/?p=2570 (last accessed on March 5, 2012)  
7. Monwar, M.M., Gavrilova, M.L.: Multimodal Biometric System Using Rank Level Fusion  Approach. IEEE Trans. on System, Man and CyberneticsPART B 39(4), 867878  (2009)  
8. Yampolskiy, R., Gavrilova, M.: Artimetrics: Biometrics for Artificial Entities. IEEE   Robotics and Automation, Magazine 19(4), 4858 (2012)  
9. Kato, T.: Database Architecture for Content Based Image Retrieval. In: Image Storage and  Retrieval Systems, pp. 112123 (1992)  
10. Eitza, M., Hildebranda, K., Boubekeurb, T., Alexaa, M.: An Evaluation of Descriptors for  Large-scale Image Retrieval from Sketched Feature Lines. Computers & Graphics 34(5),  482498 (2010)  
11. Arampatzis, A., Zagoris, K., Chatzichristofis, S.A.: Dynamic Two-stage Image Retrieval  from Large Multimedia Databases. Information Processing & Management 49(1), 274285  (2013)  
12. Swain, M.J., Ballard, D.H.: Color Indexing. Int. J. of Computer Vision 7, 1132 (1991)  
13. Daugman, J.G.: Uncertainty Relations for Resolution in Space, Spatial Frequency, and  Orientation Optimized by Two-Dimensional Visual Cortical Filters. Journal of the Optical  Society of America A 2, 11601169 (1985)  
14. Chong, C.-W., Mukundan, R., Raveendran, P.: An Efficient Algorithm for Fast Computa- tion of Pseudo-Zernike Moments. Int. J. Pattern Recogn. Artif. Int. 17(6), 10111023  (2003)  
15. Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image Retrieval: Ideas, Influences, and Trends of  the New Age. ACM Computing Surveys 40(2), Article 5, 60 pages (2008)  
16. AT&T Lab. Cambridge, http://www.cl.cam.ac.uk/research/dtg/  attarchive/facedatabase.html (last accessed on March 03, 2013)   
17. Martinezand, A.M., Avinash, C.K.: PCA versus LDA. IEEE Trans. on Pattern Analysis  and Machine Intell. 23(2), 228233 (2001)  
18. Flusser, J., Suk, T.: Rotation Moment Invariants for Recognition of Symmetric Objects.  IEEE Trans. Image Proc. 15, 37843790 (2006)  
19. Viitaniemi, V., Laaksonen, J.: Evaluating the Performance in Automatic Image Annota- tion: Example Case by Adaptive Fusion of Global Image Features. Signal Process. Image  Commun. 22(6), 557568 (2007)  
20. Chinchor, N.: MUC-4 Evaluation Metrics. In: Fourth Message Understanding Conference,  pp. 2229 (1992)    